Meeting Title: [HOLD] - Final Panel Interview Date: 2026-03-04 Meeting participants: Deepika Sethi, Kaela Gallagher, Greg Stoutenburg, Robert Tseng, Amber Lin
WEBVTT
1 00:10:21.970 ⇒ 00:10:24.980 Greg Stoutenburg: I’m trying to deal with your choice. Hello!
2 00:10:24.980 ⇒ 00:10:25.889 Deepika Sethi: Hey, Greg.
3 00:10:26.340 ⇒ 00:10:27.429 Greg Stoutenburg: Hey, good to see you.
4 00:10:27.900 ⇒ 00:10:29.340 Deepika Sethi: Likewise, how are you?
5 00:10:29.720 ⇒ 00:10:31.520 Greg Stoutenburg: I’m doing well today, how are you?
6 00:10:31.740 ⇒ 00:10:32.600 Deepika Sethi: Good to…
7 00:10:36.610 ⇒ 00:10:42.460 Greg Stoutenburg: My, my kids have spring break this week, so my, my 10-year-old is over there.
8 00:10:42.700 ⇒ 00:10:46.049 Greg Stoutenburg: putting together an Arduino Uno,
9 00:10:46.290 ⇒ 00:10:49.290 Greg Stoutenburg: Like, coding and hardware kit, so…
10 00:10:49.750 ⇒ 00:10:54.530 Greg Stoutenburg: If I appear to be trying to help someone over there, that’s who.
11 00:10:55.160 ⇒ 00:10:57.640 Deepika Sethi: I have my spring breaks next week.
12 00:10:58.150 ⇒ 00:11:04.690 Greg Stoutenburg: Yeah, yeah, yeah, it’s, I feel like winter break was, like, a minute ago, so I don’t know what all these breaks are about.
13 00:11:06.360 ⇒ 00:11:10.130 Deepika Sethi: Seriously, it’s just 3 months, and I don’t know where it all went.
14 00:11:10.610 ⇒ 00:11:11.310 Greg Stoutenburg: Yeah.
15 00:11:12.110 ⇒ 00:11:15.829 Greg Stoutenburg: Are you, are you more of a winter person, or more of a summer person?
16 00:11:15.830 ⇒ 00:11:19.440 Deepika Sethi: I’m a summer person. I don’t go out much in winters.
17 00:11:19.440 ⇒ 00:11:20.040 Greg Stoutenburg: No.
18 00:11:21.080 ⇒ 00:11:23.489 Greg Stoutenburg: Just kind of try to hide inside and be warm.
19 00:11:24.420 ⇒ 00:11:25.920 Deepika Sethi: Yeah, kind of a…
20 00:11:26.560 ⇒ 00:11:27.350 Greg Stoutenburg: Yeah.
21 00:11:32.160 ⇒ 00:11:37.659 Greg Stoutenburg: I know that, know that Robert is in New York City.
22 00:11:37.660 ⇒ 00:11:39.670 Deepika Sethi: But I spent a lot of time in California.
23 00:11:39.970 ⇒ 00:11:45.090 Greg Stoutenburg: I don’t remember where… Amber is, who’ll be on with us. So yeah.
24 00:11:45.680 ⇒ 00:11:49.119 Greg Stoutenburg: Some of us have had to develop that winter tolerance. Hey, Robert.
25 00:11:49.900 ⇒ 00:11:50.280 Robert Tseng: Hey, Greg.
26 00:11:50.280 ⇒ 00:11:51.040 Deepika Sethi: No, but…
27 00:11:51.040 ⇒ 00:11:52.889 Robert Tseng: Hi, is it Deepika?
28 00:11:52.890 ⇒ 00:11:53.890 Deepika Sethi: Yeah, that’s…
29 00:11:54.990 ⇒ 00:11:56.290 Robert Tseng: Great to meet you.
30 00:11:56.290 ⇒ 00:11:57.130 Deepika Sethi: Likewise.
31 00:12:01.210 ⇒ 00:12:11.520 Robert Tseng: Cool, I think we have the whole team assembled here, so, yeah, thanks for… I’m sorry, Greg, did you already do the intro on, like, what we’re gonna cover on this call? Nope. Nope.
32 00:12:11.530 ⇒ 00:12:13.819 Greg Stoutenburg: Just chatting for a couple minutes, waiting for y’all to sign on.
33 00:12:13.820 ⇒ 00:12:33.600 Robert Tseng: Great, appreciate it. Yeah, so I think, we sent you a case exercise, and I think we’re gonna walk through, kind of, your presentation of it. I know time is, like, we don’t have too much time, so maybe let’s just try to limit it to 10 minutes max of the presentation, so we have time to basically, kind of.
34 00:12:33.970 ⇒ 00:12:38.400 Robert Tseng: do Q&A, and then we’ll transition over to
35 00:12:38.750 ⇒ 00:12:43.689 Robert Tseng: Yeah, just sharing more about, about Brainforge, and then asking you some, some, some, like.
36 00:12:44.480 ⇒ 00:12:49.179 Robert Tseng: more behavioral questions, I guess. How does that sound for a structure?
37 00:12:49.750 ⇒ 00:12:50.730 Deepika Sethi: I think that sounds good.
38 00:12:51.120 ⇒ 00:12:57.629 Robert Tseng: Okay, great. Well, yeah, well then we’ll let you kind of… we’ll turn it over to you, if you don’t mind kind of just sharing your screen.
39 00:12:58.220 ⇒ 00:13:12.989 Robert Tseng: I don’t exactly know what format you presented it in. I believe that we asked for a deck, or some people do PDFs reports, so I don’t think we were too particular about it. We just wanted to have something to follow along on as you’re walking us through it.
40 00:13:13.410 ⇒ 00:13:16.900 Deepika Sethi: Okay, so probably I’ll just share my screen first, let’s.
41 00:13:16.900 ⇒ 00:13:17.500 Robert Tseng: Sweet.
42 00:13:17.700 ⇒ 00:13:18.760 Greg Stoutenburg: Sure.
43 00:13:24.290 ⇒ 00:13:27.000 Deepika Sethi: Please let me know if my screen is visible.
44 00:13:31.530 ⇒ 00:13:32.130 Robert Tseng: Yes.
45 00:13:32.130 ⇒ 00:13:32.490 Greg Stoutenburg: There we go.
46 00:13:32.490 ⇒ 00:13:33.109 Robert Tseng: See that?
47 00:13:33.630 ⇒ 00:13:36.310 Deepika Sethi: Hopefully, it’s also visible in the slide mode.
48 00:13:36.660 ⇒ 00:13:37.590 Deepika Sethi: Is it?
49 00:13:37.830 ⇒ 00:13:38.810 Robert Tseng: Yep, perfect.
50 00:13:39.250 ⇒ 00:13:50.630 Deepika Sethi: Great. So I’ll just quickly walk you through with my approach and what I could think about from the data, because it was very limited data for a… I think it was hardly a month’s data, right?
51 00:13:50.760 ⇒ 00:14:10.579 Deepika Sethi: So my approach had been, like, to quickly do a little… I started with the problem statement. The problem had been, you know, I could see that data was across 4 stores in Cincinnati, and we were trying to identify the challenges, growth opportunity, whatever we could, in something called Dash Mart, which is more of a grocery store, and has multiple verticals.
52 00:14:11.950 ⇒ 00:14:23.040 Deepika Sethi: So, content, I’ve tried to keep it very simple. One is, first, we go through the overall, or the business overview and summary, and then we start looking at the recommendation, and then we start going into
53 00:14:23.140 ⇒ 00:14:26.730 Deepika Sethi: Comparison across stores and the individual store analysis.
54 00:14:28.080 ⇒ 00:14:37.320 Deepika Sethi: So these are some assumptions, because I could see that some of the data was missing, especially when it comes to Dasher ID and delivery class. I have,
55 00:14:37.670 ⇒ 00:14:52.270 Deepika Sethi: noticed those, but really not imputed it, because it might… considering the data was really less, it would have… it might have really not provided a real picture, so I prefer to still have those in our… in my analysis.
56 00:14:52.650 ⇒ 00:14:55.310 Deepika Sethi: And again, the… another thing to note was, like.
57 00:14:55.490 ⇒ 00:15:14.169 Deepika Sethi: Delivery, missing incorrect report, I’m assuming it is because of, no dasher assigned, and it is a customer report. Sorry, my bad. Delivery missing is something post-delivery and is reported by customers, so that, obviously, after they have looked at their final,
58 00:15:14.180 ⇒ 00:15:17.989 Deepika Sethi: What do I say, final order, which is… which is delivered to them.
59 00:15:18.770 ⇒ 00:15:37.989 Deepika Sethi: So this is a business overview. The data was… had multiple rows per delivery, right? So I tried… what I did was, though for my charts and other things, I didn’t really denormalize it, but I did notice, a few things that I ensured that I take
60 00:15:38.220 ⇒ 00:15:45.550 Deepika Sethi: everything per delivery, and not exactly per row of Excel. Like, total deliveries were around 13,000, and then
61 00:15:45.640 ⇒ 00:16:03.379 Deepika Sethi: Considering day-wise delivery, it was, like, 438, some unique items, and what was our average order values, late rate, cancellation rate, missing report, whatever I could really figure out from the sheet, because I don’t think I had any more data or any more reference around that, so I could really work on the rates.
62 00:16:03.380 ⇒ 00:16:08.109 Deepika Sethi: percentages of different kind of, information that I could look at it.
63 00:16:08.450 ⇒ 00:16:25.060 Deepika Sethi: things which stood out was, like, Dash Mart really dominates the entire one. Grocery 1, 2, and 3 were forming the lower part of, or maybe had a lesser share in the overall deliveries and overall, overall data that I had.
64 00:16:25.210 ⇒ 00:16:35.180 Deepika Sethi: Additionally, some of the categories which had maximum missing rates were pantry and frozen. Other than that, one thing that also stood out was
65 00:16:35.360 ⇒ 00:16:38.599 Deepika Sethi: A lot of dashes only had one delivery in the entire month.
66 00:16:39.230 ⇒ 00:16:48.549 Deepika Sethi: And since there was no information around how the dashes are assigned and everything, I thought it would be a good point to just show it, just ensure that this is being noticed.
67 00:16:49.170 ⇒ 00:16:54.289 Deepika Sethi: Based on the entire dataset, there are four recommendations that I suggested.
68 00:16:54.430 ⇒ 00:17:09.450 Deepika Sethi: And I’ll try to have it in a way, like, short-term, mid-term, and the long-term. The first short-term, description, or first short-term recommendation would be the inventory transparency. If you would have noticed, like, a lot of missing items, and then,
69 00:17:09.589 ⇒ 00:17:26.000 Deepika Sethi: especially, I think it’s, grocery 2 stores which is trying to substitute a lot of items with either gross category substitution or the same category substitutions, right? So, I believe if, we do have, softwares in the market which can have with
70 00:17:26.000 ⇒ 00:17:44.719 Deepika Sethi: Which can really work with, operations, inventory operations and management, and whatever missing items are there, we are not either delivering or substitution, those could be really used, and we can ensure that, based on our customer data and the order data, we can ensure we have most of the required items out of those.
71 00:17:46.310 ⇒ 00:17:56.729 Deepika Sethi: Then again, the assumption here is that it will be quick to integrate with the new inventory system if there’s anything in place, plus the employees will be quick to onboard.
72 00:17:57.760 ⇒ 00:18:12.880 Deepika Sethi: Second recommendation is, again, short-term, which is around doing some trainings for the staff. If you, again, notice, I know Dashmat has… Dashmat 1 had the minimum missing rate, but still, the substitution rate was lesser than the other one… other tools.
73 00:18:12.880 ⇒ 00:18:22.229 Deepika Sethi: Probably, some sort of training might help us to substitute the missing items better, and lead to maybe better customer satisfaction.
74 00:18:22.630 ⇒ 00:18:36.129 Deepika Sethi: Second was for grocery 1, where I could notice a lot of missing items or incorrect items in the order. So, that’s where I thought, straining would be a quick short-term gain, and, you know, to take…
75 00:18:36.490 ⇒ 00:18:42.369 Deepika Sethi: To really, have low hang… to have low-hanging fruits in order, so we can do that.
76 00:18:43.200 ⇒ 00:18:49.100 Deepika Sethi: Additionally, in midterm, it will become, as I already mentioned, 46% of dashes delivered only
77 00:18:49.330 ⇒ 00:18:52.699 Deepika Sethi: One order, so maybe having
78 00:18:52.900 ⇒ 00:19:01.400 Deepika Sethi: better management of dashes would be really, good, and I know it’s not something that can be done, like, quickly. You need to understand why
79 00:19:01.400 ⇒ 00:19:14.680 Deepika Sethi: there are 46 dashes… sorry, 46% dashers delivering one. Is it a gig worker? Are there any other reasons? Were they not satisfied? So, a lot of analysis needs to go in, so this becomes a mid-term recommendation.
80 00:19:15.620 ⇒ 00:19:23.799 Deepika Sethi: Then fourth is probably a longer term, because it will be scaling the entire business model, maybe to new markets, and to ensure that,
81 00:19:23.970 ⇒ 00:19:32.279 Deepika Sethi: This market is not impacted, which is the late-night deliveries. As we start drilling down, you’ll notice that most of the deliveries start
82 00:19:32.620 ⇒ 00:19:48.390 Deepika Sethi: afternoon and go beyond, like, 2 or 3, and that’s the high time, and then there comes the downtime in our, in the delivery and that. So, probably, we know that there’s one thing working in this market, which is late-night delivery, and it could be scaled to another, or new markets.
83 00:19:49.830 ⇒ 00:19:52.770 Deepika Sethi: So this is a store-wise comparison on,
84 00:19:53.100 ⇒ 00:20:08.560 Deepika Sethi: deliveries, or share orders, average orders, number of dashes and everything. Again, I think I did mention that, Dash Mart 1 has around 69% share, but the average order value here is pretty low. Similarly, if you will look at,
85 00:20:09.290 ⇒ 00:20:18.899 Deepika Sethi: Excuse me. Grocery to the average order, or the order size here is the maximum. However, the cancellation rate is equally high in this store.
86 00:20:21.650 ⇒ 00:20:24.590 Deepika Sethi: Okay, if you have any questions, otherwise, I’ll just keep going.
87 00:20:27.070 ⇒ 00:20:31.520 Robert Tseng: Yeah, feel free to keep going. We, you know, we usually ask questions at the end,
88 00:20:31.520 ⇒ 00:20:32.010 Deepika Sethi: Coffee.
89 00:20:32.010 ⇒ 00:20:32.570 Robert Tseng: Yeah.
90 00:20:33.120 ⇒ 00:20:46.409 Deepika Sethi: Okay, then there’s a little more of that store-wise comparison. This is more around on the time for the dasher to reach store, and time to accept deliveries. This is my experience with being a consumer, that,
91 00:20:46.820 ⇒ 00:21:11.689 Deepika Sethi: if my order is accepted quickly, I just somehow get that feeling, okay, I’ll be getting it quickly, right? If it takes time, even though if it comes in the similar time frame, somehow, mentally, it really doesn’t go well. So, same thing I’ve used here, you know, trying to understand how quickly are we accepting the orders, and how much time is the dasher taking to move. For every store, I have done a store-wise comparison, like, how much percent each store is, ensuring that
92 00:21:11.690 ⇒ 00:21:13.479 Deepika Sethi: So, the deliveries are accepted.
93 00:21:13.500 ⇒ 00:21:24.519 Deepika Sethi: I know there were some null timings, I’m just assuming this is a data challenge, but I just wanted to make sure that in future this data is being handled, so I’ve kept it in the chat.
94 00:21:26.040 ⇒ 00:21:35.410 Deepika Sethi: Okay, and again, notice, if you notice the overall pattern, 20% customers have to really wait quite long, and some of the outliers are being there.
95 00:21:39.710 ⇒ 00:21:55.720 Deepika Sethi: Then, this is the… again, we are starting with the store-wise comparison. Again, this is around the missing items, and everything I’ve… whenever there’s a comparison, I’ve tried to keep it in percentages because of the numbers. A lot of difference in the numbers for each of the stores.
96 00:21:55.720 ⇒ 00:22:01.239 Deepika Sethi: So if you’ll see, overall pantry items and the frozen items are most of the missing items.
97 00:22:01.480 ⇒ 00:22:04.830 Deepika Sethi: And store… grocery store, too, does come.
98 00:22:05.250 ⇒ 00:22:13.810 Deepika Sethi: Sorry, Grocery store 1 does come out, you know, gets highlighted for the percentage of missing items, and that’s where it was coming, you know, kind of,
99 00:22:14.070 ⇒ 00:22:24.580 Deepika Sethi: having some sort of screening for them. Then, missing items is one thing. The other thing to look at is whether stores or the staff is able to substitute it quickly.
100 00:22:24.610 ⇒ 00:22:41.020 Deepika Sethi: So, the second chart shows the same thing, like, how much percentage of the missing items has each store been able to really substitute. And here you will see, though gross dash mat 1 has, like, least missing items, but their substitution rate is also very low.
101 00:22:41.080 ⇒ 00:22:45.490 Deepika Sethi: So, probably, even, they can really work on those trainings.
102 00:22:48.140 ⇒ 00:23:01.709 Deepika Sethi: Then this is around… up till now, we were talking about items, missing items, how much is the store order, and ticket sizes, but now I’ve started to look at the incorrect percentages, cancellation and late percentages across store.
103 00:23:02.950 ⇒ 00:23:07.779 Deepika Sethi: Again, to notice is this was all overall values. Now,
104 00:23:08.260 ⇒ 00:23:26.820 Deepika Sethi: These incorrect percentages, late percentages, and cancellation is per days of the week, and I’ve tried to do the conditional formatting just to see how stores are looking with respect, in comparison to each other. A few things that really took me out of notice was this grocery tours.
105 00:23:26.860 ⇒ 00:23:32.450 Deepika Sethi: lead percentages on Wednesdays and Thursdays, probably there was something either less staff.
106 00:23:32.950 ⇒ 00:23:39.910 Deepika Sethi: Less dashes or some other, macroeconomic, like, traffic or something, which is really hampering those two days.
107 00:23:40.140 ⇒ 00:23:51.669 Deepika Sethi: And cancellation rate for grocery 2 is 9.52 on, Friday. Again, for grocery 3, you will notice a lot of, percentages are, like.
108 00:23:51.670 ⇒ 00:24:06.939 Deepika Sethi: approximately zero, but then the share of grocery 3 is very, very low. So, they are very low activity, so these numbers may not really be, that well. We need to dig deep into grocery 3 activities in that case.
109 00:24:08.810 ⇒ 00:24:14.379 Deepika Sethi: Then, I just tried to do a store-wise analysis, where it starts with dash mod1.
110 00:24:14.650 ⇒ 00:24:19.930 Deepika Sethi: One thing to notice at every store is that this period, particularly from 12am to
111 00:24:19.960 ⇒ 00:24:39.429 Deepika Sethi: around 4, 5, or 6, or early in the morning till… not early, but till 10 a.m. in the morning. It’s almost like no volume of data… volume of orders, and a very high D2R ratio, and no availability of the CLAT time, which is, like, the acceptance time.
112 00:24:39.900 ⇒ 00:24:59.070 Deepika Sethi: Now, again, the data was very limited, so I couldn’t really create a causal… causal relationship, but I’m assuming, it… both of these might be impacting each other. It might be that because dashes are not available, we are not accepting, so lately the… or due to that, the volumes have been very down for this… this time period.
113 00:25:00.250 ⇒ 00:25:05.050 Deepika Sethi: And, so you will also see the cancellation also spike at this time.
114 00:25:05.330 ⇒ 00:25:20.800 Deepika Sethi: And… or maybe because we hardly get, orders, the staff is also not that very active and efficient, and you will see a spike in incorrect value as well, incorrect, percentages as well, for Dash Mart 1.
115 00:25:21.640 ⇒ 00:25:38.639 Deepika Sethi: Then come grocery 1 analysis. Again, pretty similar. Late night deliveries are high, hour, 3 to 6 a.m. period is very inefficient, and very low, volume, and the volume starts spiking later.
116 00:25:39.560 ⇒ 00:25:58.169 Robert Tseng: Hey, Deepika, yeah, I think, thank you so much for walking through. I mean, let me… can I… can I just pause you there? Just because I want to be mindful of time as well. Great. Yeah, I mean, I think… let me just, let me just kind of speed us to the… to the end of, like, kind of your presentation here. I mean, overall, like, I think the analysis looks great,
117 00:25:58.170 ⇒ 00:26:01.379 Robert Tseng: I mean, I want to just call out a couple things, you know, like on…
118 00:26:01.380 ⇒ 00:26:16.390 Robert Tseng: slide 10, like, store… the store comparison slide, like, I think really great that you went to that level of granularity doing store comparisons. Like, I think that’s… that’s a… that’s a big… that’s a big plus. Not getting stuck in, like, averages, but, like, really trying to
119 00:26:16.510 ⇒ 00:26:20.680 Robert Tseng: Kind of go a layer deeper so that we could actually do some comparative analysis.
120 00:26:20.680 ⇒ 00:26:43.079 Robert Tseng: I really like, like, the table that you put together here, too. Super clean, very easy to understand. And then, clearly, you know your way around Tableau, your analysis, so no doubt on your ability to drill into things here. So I just want to orient us on, like, you know, obviously, you’re quite senior, one of the stronger candidates coming through this… coming through this process, so no need to kind of go too much into the analysis, I think.
121 00:26:43.080 ⇒ 00:27:07.119 Robert Tseng: I think it’d be easy… I think it’d be better for us to assess, just kind of, like, recommendations, and kind of give you more real examples of, kind of the types of environments that… the types of decisions and… or the types of situations that we’re… that we face on clients, and I think that would probably be a better way for us to, like, really, kind of qualify you on what’s, like, not
122 00:27:07.420 ⇒ 00:27:13.249 Robert Tseng: I think you’ve already kind of passed past the bar at this point for the analysis. Is that okay?
123 00:27:13.570 ⇒ 00:27:14.710 Deepika Sethi: Yeah, that works perfectly, then.
124 00:27:14.710 ⇒ 00:27:15.830 Robert Tseng: Okay, great.
125 00:27:15.830 ⇒ 00:27:36.669 Robert Tseng: Yeah, so I just want to talk through your recommendations. So yeah, I think the short term, medium-term, long-term, great. I really appreciate that, too. I think clients will expect that from us. Just to give you some context from, like, our business, and then kind of go back into the case, our average contract length is about 3 months, currently. And so,
126 00:27:36.670 ⇒ 00:27:44.859 Robert Tseng: we don’t really get that much time to really make a big impact, and so the short term is super important for us. So definitely when, you know, when…
127 00:27:44.860 ⇒ 00:28:08.700 Robert Tseng: I think we’re often having to go into kind of very ambiguous data environments, like this data set, and just to learn about their business in a creative way that, like, they may not be able to as well. So, I think you’re really structured in your thinking, you’re pretty opinionated about where you should go and dig into, so I think you’d be able to find your way around a messy data set, as demonstrated here. But now I’d like to kind of jump into
128 00:28:08.700 ⇒ 00:28:14.430 Robert Tseng: kind of asking specific questions, just to role-play through the short-term recommendations.
129 00:28:14.940 ⇒ 00:28:17.259 Robert Tseng: Yeah, so let’s kind of talk about the first one.
130 00:28:17.340 ⇒ 00:28:31.970 Robert Tseng: Yeah, so you’re saying that, hey, like, maybe there’s, yeah, there’s, like, a transparency or visibility problem here. The missing rates are really high, you feel like this is very prevent… preventable, you’re alluding to there could be a data… there could be a data issue.
131 00:28:31.970 ⇒ 00:28:55.490 Robert Tseng: Or maybe, like, it’s something else. I think that’s a totally… that’s a very realistic situation that we run into, where we run… we see data gaps where it just… something doesn’t make sense. Like, it’s… it should be, like, I don’t know why there’s, like, a gap in the systems, and we would have to either go investigate that, or there’s some… or there’s something else. So, can you walk me through a time, specifically, when you had to deal with something like this?
132 00:28:55.780 ⇒ 00:29:02.099 Robert Tseng: And, yeah, like, I guess, what was your approach to being able to reconcile the data gap?
133 00:29:03.480 ⇒ 00:29:11.059 Deepika Sethi: So, Robert, I think we… do you want me to keep it, just the case study, or my overall experience around…
134 00:29:11.350 ⇒ 00:29:12.010 Deepika Sethi: things.
135 00:29:12.010 ⇒ 00:29:23.999 Robert Tseng: Yeah, no, I think I’m comfortable with you using your personal experience. Like, I think, you know, if we talked about this case study, it would all kind of be hypothetical, so I’d rather hear kind of your experience, yeah.
136 00:29:24.380 ⇒ 00:29:40.210 Deepika Sethi: So, I think, we haven’t talked about my experience. I didn’t really work as a data analyst, but I did work as a product owner and business analyst, where there comes a lot of… there comes time when users are really not sure what’s happening, they don’t even know their data, and data quality is a mess.
137 00:29:40.590 ⇒ 00:29:54.480 Deepika Sethi: So, in my last role with the London Stock Exchange Group, I was working for operational resilience, data governance, where it was more like understanding and ensuring the data pipelines are good. So, how I approached
138 00:29:54.910 ⇒ 00:30:08.440 Deepika Sethi: to understand the data was, first, identify… because it was a pretty big firm, so processes were more or less more structured than a smaller firm, so it would start with creating very high-level process flows, understanding the flows.
139 00:30:08.440 ⇒ 00:30:22.700 Deepika Sethi: Identifying the… and using those workflows and process flows, trying to understand the critical data elements, and then map those elements with available data, and see whether we have that, sort of data with us.
140 00:30:22.700 ⇒ 00:30:32.909 Deepika Sethi: And if yes, that’s good and great. If not, then probably dig deep into the data, analyze what’s really missing, what could really be added, and then kind of creating some sort of
141 00:30:32.910 ⇒ 00:30:52.049 Deepika Sethi: visualization, not exactly very full-fledged, but some sort of stats, like what is missing, what is required, what as per current situation should have been there, like we were talking about application downtime. You know, if I couldn’t really… that’s a very important time when it comes to operational resilience, but if I couldn’t really find that out in my data.
142 00:30:52.350 ⇒ 00:30:55.980 Deepika Sethi: This is a very big gap, so identifying those gaps.
143 00:30:56.320 ⇒ 00:31:10.840 Deepika Sethi: listing those kind of… documenting those, or lean documentation, and going back to users and the tech teams, and the authorized source to understand how we can really fill those gaps, and probably try to come up with a solution, which is something that…
144 00:31:10.910 ⇒ 00:31:18.629 Deepika Sethi: That is maybe 6, 30, 60, 90 days plan, how we can quickly start taking the value out of the,
145 00:31:18.930 ⇒ 00:31:27.749 Deepika Sethi: We can quickly start delivering, values in shorter term, but make sure that strategic solution is also implemented, and it’s not just technical one.
146 00:31:28.440 ⇒ 00:31:40.100 Robert Tseng: Got it. Yeah, no, I appreciate that example. Yeah, I think for our… we’re not really serving, like, enterprise clients right now. I mean, we have a couple that are kind of at that tier, I would say, where we can really, like.
147 00:31:40.110 ⇒ 00:31:43.499 Robert Tseng: Put out, like, a 3-month, kind of business process change.
148 00:31:43.500 ⇒ 00:32:08.219 Robert Tseng: So I think, like, you know, maybe… I’m just trying to slot you in terms of, like, in our portfolio, I want you to have a sense of, like, what you’re… what you would be getting yourself into if you walked into our portfolio as well. We’re kind of a mix of healthcare and CPG, so… and there’s a few SaaS as well. On the healthcare side, I definitely think that what you’re describing makes sense. It sounds like you’ve worked in really
149 00:32:08.220 ⇒ 00:32:26.430 Robert Tseng: regulated environments where there are a lot of complex business processes. On the CPG side, I feel like that would probably be a little bit over… it’d probably be too much, which is… it’s good context for us to know, kind of, like, where your skill set would be able to fit. But yeah, I think I’m just trying to give you, like, live…
150 00:32:26.430 ⇒ 00:32:33.440 Robert Tseng: live response on, kind of, how I’m interpreting, kind of, your, your response… your answers to the questions that we’re giving.
151 00:32:34.310 ⇒ 00:32:39.790 Robert Tseng: I’m gonna ask one more question, then I’ll let the team kind of jump in as well. The second recommendation.
152 00:32:39.790 ⇒ 00:33:04.390 Robert Tseng: So, you talk about, yeah, kind of staff training, yeah, you realize, like, hey, maybe this isn’t, like, a systems problem, maybe it’s not, like, data pipelines are broken, or, there’s just, like, a, yeah, like a systems gap, and it’s actually just, like, the performance of the staff in a pretty manual operation is just not great. So, have you worked in an environment where you’ve had to
153 00:33:04.390 ⇒ 00:33:05.590 Robert Tseng: basically.
154 00:33:06.230 ⇒ 00:33:29.410 Robert Tseng: push for this type of training, or, I’m just curious, like, kind of, like, how… how far, like, your… your experience has… has gone in terms of taking your analysis, or kind of your process, and being able to actually, implement it with, like, a warehouse operator, or… or some other kind of really non-technical, really operational stakeholder.
155 00:33:30.660 ⇒ 00:33:51.800 Deepika Sethi: Okay. Again, my experience all comes from large enterprise, so I haven’t really worked in warehouses, but I do have worked with a number of people, cross-team collaboration, where I do see one team missing something, or not having, not having at the same position as the other team. Like, business team knows more about domain, tech teams know more about technology.
156 00:33:51.800 ⇒ 00:33:55.239 Deepika Sethi: So I kind of think for every problem, be it,
157 00:33:55.630 ⇒ 00:34:12.510 Deepika Sethi: in warehouse or in a big enterprise, there are 3 or 4 aspects to it. People, processes, and data. You just need to figure out where your problem lies, and this is… and you will always see problems in these three categories, and nowadays, governance and tech systems are also coming up.
158 00:34:12.770 ⇒ 00:34:16.709 Deepika Sethi: So, when it comes to people, one thing always works, trying to understand
159 00:34:16.920 ⇒ 00:34:34.569 Deepika Sethi: where they are currently, and how you can really improve, and there’s always the room for improvement. And I’ve seen, some minor trainings always help to increase the efficiency. That’s where I came from. While I was working with Dodge Bank, I was working across teams product.
160 00:34:34.760 ⇒ 00:34:51.260 Deepika Sethi: technology and business. They all didn’t really understand each other. But having some small training sessions and not really full-freshed training sessions helped us to ensure that those people started understanding problems from each other’s and, you know, working better and getting better efficiency out of each team.
161 00:34:51.400 ⇒ 00:34:55.530 Deepika Sethi: So that’s where I thought, you know, it might not really be a… Bro.
162 00:34:55.670 ⇒ 00:35:02.139 Deepika Sethi: All kind of, processor data issue, it might more be nobody really focus on the people, or…
163 00:35:04.010 ⇒ 00:35:10.620 Robert Tseng: Okay, thank you. Yeah, I guess Greg and Amber, I’ll kind of let you ask questions. I… yeah.
164 00:35:13.790 ⇒ 00:35:32.700 Greg Stoutenburg: Yeah, thanks for this. I… I have a question, so also about the recommendations. So, just looking at the recommendations, I, I, you know, I’m sure that they’re in order, right? There’s a reason why it goes recommendation 1, then 2, then 3, then 4, because this is, like, a list of priority. This is the way that… this is the order in which you’d approach these, right?
165 00:35:33.020 ⇒ 00:35:34.250 Deepika Sethi: Yeah, exactly.
166 00:35:34.650 ⇒ 00:35:44.050 Greg Stoutenburg: Okay, so just looking at the impact for each of the recommendations on each slide, I was hoping you could speak to…
167 00:35:44.050 ⇒ 00:35:49.209 Greg Stoutenburg: What made… knowing that this is the expected impact of each of these, so not questioning that at all.
168 00:35:49.210 ⇒ 00:36:04.999 Greg Stoutenburg: What made you decide that recommendation 1 is recommendation 1 because of this impact, and Recommendation 2 is Recommendation 2 because of that expected impact, and so on? Could you just sort of, like, give a little bit of a narrative around your thinking for how you… how you prioritize these?
169 00:36:05.880 ⇒ 00:36:25.719 Deepika Sethi: Okay, so, when it comes to prioritization, I had looked at a number of things. One is impact, which is quite, visible in the slides. Second is how quickly, like, timelines and the feasibility, right? So, if you look at recommendation 3 and 4, those feasibility is medium, and those are pretty long-term. You know,
170 00:36:25.990 ⇒ 00:36:39.659 Deepika Sethi: I truly believe to maximize the value and try to, you know, catch the low-hanging fruits quickly, it kind of helps to, you know, build user trust, so that’s what my,
171 00:36:40.140 ⇒ 00:36:43.020 Deepika Sethi: Thought process had been to look at multiple things, like,
172 00:36:43.100 ⇒ 00:36:49.350 Deepika Sethi: What’s the impact, what’s the timeline, and what’s the value that I’m gonna get out of this, and what’s the feasibility?
173 00:36:49.350 ⇒ 00:37:05.629 Deepika Sethi: And so for first and two, I think both of these are high feasibility and short term, but why I said inventory system to be in place? Because of the… if you will look at the impact, there were around… if you go through your 60,000 rows, there are around 3,500 missing items per month.
174 00:37:05.630 ⇒ 00:37:21.349 Deepika Sethi: Because it was a monthly data, so it’s an assumption that it is there. And I believe there are already a number of companies who… which have implemented inventory systems, and it ranges from… I know the cost ranges from 50,000, depending on the type of
175 00:37:21.350 ⇒ 00:37:28.550 Deepika Sethi: industry you are in, and the size of the industry, but I think, we have these, this particular thing established.
176 00:37:29.070 ⇒ 00:37:41.080 Deepika Sethi: And we can get the guidance from the industry, so it might be very quick to implement, and might really have more impact, considering we have around 3,500 missing items, and average ticket rate is around…
177 00:37:41.100 ⇒ 00:37:50.140 Deepika Sethi: Average item value goes around $5, so probably this might help us, this might be delivering more value than the substitution.
178 00:37:50.720 ⇒ 00:38:02.989 Deepika Sethi: And you can substitute only when you have an idea on the inventory, right? So that’s why the recommendation one was, first, and then second was training, and the rest two were, like, long-term or mid-term recommendations.
179 00:38:04.390 ⇒ 00:38:06.409 Deepika Sethi: Yeah. Avant version.
180 00:38:06.410 ⇒ 00:38:20.820 Greg Stoutenburg: Yeah, that’s good, thanks for that. So, so thinking about, like, thinking about the impact again, sorry to seem obsessed with just one thing, but, you know, that’s how it is. You find your thing, and then you ask questions about it. So, looking at all of these, right, so the…
181 00:38:21.430 ⇒ 00:38:27.129 Greg Stoutenburg: The impacts you’ve listed are, 35 fewer… 3,500 fewer missing items per month.
182 00:38:27.200 ⇒ 00:38:45.160 Greg Stoutenburg: higher consumer satisfaction reorder, increased substitution rate, better fulfillment, better satisfaction, increased order rate, better fulfillment rates, new competitive moats, incremental revenue growth, higher customer retention. What does that roll up to, right? So, like, thinking about the business perspective on this, if the client is,
183 00:38:45.290 ⇒ 00:38:55.949 Greg Stoutenburg: Right? The client’s some stakeholder at some company that’s, you know, they know that they have a deficiency, so that’s why they’re coming to you to help them with the problems that they’re seeing.
184 00:38:56.550 ⇒ 00:39:02.450 Greg Stoutenburg: All these different impact statements, all these different impact metrics, like, what do they roll up to, so that when… when…
185 00:39:02.480 ⇒ 00:39:17.290 Greg Stoutenburg: the reason I’m asking this question is, when that stakeholder has to go to their board, or maybe the stakeholder is on the board, what does… what do those impact statements cash out in terms of, like, what does this roll up to as far as how it’s going to move the business forward?
186 00:39:17.540 ⇒ 00:39:19.210 Greg Stoutenburg: And just…
187 00:39:19.210 ⇒ 00:39:27.590 Deepika Sethi: Probably one metric you are looking at which it will impact from the business point of view, is that right… a reframing of your question, Greg?
188 00:39:30.040 ⇒ 00:39:32.010 Greg Stoutenburg: Sorry, say that second part again?
189 00:39:32.250 ⇒ 00:39:38.459 Deepika Sethi: So I was saying, if I get your question correct, we are talking about that one metric which this impact will,
190 00:39:38.710 ⇒ 00:39:40.300 Deepika Sethi: kind of,
191 00:39:40.870 ⇒ 00:39:52.839 Deepika Sethi: will have, will further enhance, or will show, basically, ultimately, what does that, rolls up into the final metric, right? That’s what you’re looking at.
192 00:39:53.450 ⇒ 00:40:01.219 Greg Stoutenburg: Yeah, things like that. So, like, as far as… as far as the business value of these suggestions, like, like, what is it? So, if it helps…
193 00:40:01.220 ⇒ 00:40:13.980 Greg Stoutenburg: You know, if I’m the person who’s responsible for customer satisfaction, then I see the slides that say the impact is higher customer satisfaction, and I’m like, good, this helps me. If I’m, like.
194 00:40:13.980 ⇒ 00:40:19.010 Greg Stoutenburg: the Chief Revenue Officer, maybe I look at slide 1, and I’m like.
195 00:40:19.070 ⇒ 00:40:35.700 Greg Stoutenburg: okay, 3,500 fewer missing items per month, like, alright, I’d love for it to be zero in some kind of, you know, perfect world, but, you know, I don’t… I don’t see that that actually helps me with my… the metrics that I really care about. So, yeah, can you just speak to that a little bit?
196 00:40:36.510 ⇒ 00:40:51.769 Deepika Sethi: I would say it will probably be more of a customer retention strategy in this case, because what we are talking about is, you know, ensuring that the experience of the current customer is good, and we are kind of,
197 00:40:52.000 ⇒ 00:41:04.470 Deepika Sethi: Creating high barrier to… high switching costs for customer once they are pretty satisfied with the speed and everything. We’ll be able to retain them, hopefully, and they’ll not switch to any other competitor.
198 00:41:05.090 ⇒ 00:41:09.879 Deepika Sethi: That’s how I’m looking at it. I hope I’ve got your question correctly.
199 00:41:10.450 ⇒ 00:41:29.730 Greg Stoutenburg: Okay, yeah, I like that. Can I give you just, like, one hypothetical? So let’s say, let’s say that I’m the client, and you’ve given me your deck, and I’m like, this sounds great. I’ve got, like, 2 other people on my team, and we want to execute something in the next 3 weeks.
200 00:41:29.950 ⇒ 00:41:41.570 Greg Stoutenburg: what should we do? So, none of these slides is exactly a fit for what I’m asking for. You have to reduce it right now. Like, what’s gonna be the highest impact thing that we can do right now to move the needle on some metric of importance?
201 00:41:43.060 ⇒ 00:41:46.110 Deepika Sethi: Okay, so put next 3 weeks, right?
202 00:41:47.420 ⇒ 00:41:49.269 Deepika Sethi: Okay, let me just,
203 00:41:49.270 ⇒ 00:41:50.140 Greg Stoutenburg: Yeah, we got 3 weeks.
204 00:41:50.140 ⇒ 00:41:56.669 Deepika Sethi: It’s fine if I just look around the slides to quickly make it up in my mind.
205 00:41:57.780 ⇒ 00:41:58.530 Greg Stoutenburg: Yeah, sure.
206 00:41:59.140 ⇒ 00:42:04.339 Deepika Sethi: Or even just thinking about, like, your view overall of this dataset and your recommendations, like.
207 00:42:04.340 ⇒ 00:42:11.220 Greg Stoutenburg: What do you think is just the… the fastest to implement, highest impact thing that we could do today?
208 00:42:12.310 ⇒ 00:42:26.200 Deepika Sethi: Yeah, I’m just, thinking about it, just getting in sure that I remember every information correctly. Then I think in that case, there will be two things which are coming on top of my head. One will be to go to Dashmart to
209 00:42:27.600 ⇒ 00:42:34.939 Deepika Sethi: One for the substitution training, because that’s something can be done quickly. If they’re able to substitute, it will be, like, good.
210 00:42:35.260 ⇒ 00:42:42.190 Deepika Sethi: Second is around, some sort of efficiency, you know.
211 00:42:42.430 ⇒ 00:42:48.710 Deepika Sethi: If you will see, this part, where we have this incorrect
212 00:42:49.090 ⇒ 00:43:05.779 Deepika Sethi: items being delivered. That’s also something we can quickly, ensure that, you know, along with the efficiency, we are all… the staff has either some review process or something to ensure that we are not really sending incorrect items.
213 00:43:05.830 ⇒ 00:43:13.160 Deepika Sethi: Which, ultimately will help, having better customer satisfaction if they don’t have to report back on the incorrect items.
214 00:43:13.800 ⇒ 00:43:26.120 Deepika Sethi: Because for every other thing, I think we might have a dependency on dashers, or maybe some other system, but these are something which could be done independently in each store, rather than really looking at the full picture.
215 00:43:27.640 ⇒ 00:43:30.220 Greg Stoutenburg: Yeah, cool. Awesome. Thanks, I appreciate that.
216 00:43:35.430 ⇒ 00:43:44.710 Robert Tseng: Okay, great. I think we can kind of come out of the exercise. You can stop sharing your screen, Deepika. Yeah, I’d really appreciate the time that you put into this exercise, and…
217 00:43:44.750 ⇒ 00:43:58.829 Robert Tseng: Yeah, I know it’s been kind of a long, drawn-out process here, and we’re obviously as… I mean, we’re a small startup, we’re, like, around 25 people, kind of, like, I think we were trying to throw you a few scenarios of, like, thinking quick on your feet,
218 00:43:58.830 ⇒ 00:44:19.690 Robert Tseng: a client wants something that should have been answered yesterday, like, that’s the kind of environment that we’re dealing with a lot of the time. Only with, like, the clients that we’ve been rolling with for, like, you know, a year, year and a half at this point are we really in a place where we’re able to be the strategic partner, and I really see you being that… a good fit for that type of client.
219 00:44:19.690 ⇒ 00:44:25.689 Robert Tseng: I’m curious, like, kind of, why, why are you considering Brainforge? You know, I feel like, you know, based on what we’ve described, like.
220 00:44:25.720 ⇒ 00:44:38.829 Robert Tseng: Seems like, you know, you’ve… you’re working with a much more… much larger, more complex… more complex organizations, like, yeah, like, kind of, I just want to… I’ll leave that open-ended, I just want to hear, kind of, what… what your thinking is there.
221 00:44:39.130 ⇒ 00:44:51.519 Deepika Sethi: Also, there are a few things that actually came to my mind. Again, while I was working in my last role, I came across a little cutting-edge technologies, and I felt I really needed to go back, and I had been working with data, and I could realize that
222 00:44:51.520 ⇒ 00:45:08.269 Deepika Sethi: Data is the thing to go, right? Data is the new oil now. Plus, larger organizations are not that very agile. It’s very difficult to get everyone on board and really implement the solution. There are times when any solution which could really be implemented in a day just gets stuck because you didn’t have an overboard.
223 00:45:08.300 ⇒ 00:45:21.680 Deepika Sethi: I have a business owner, so that’s where Brainforce came in. I was looking for opportunities as I’m about to get eligible for working, so I was looking what I really need to do, and Brainforce deals in both of the areas which I was looking forward to, which is
224 00:45:21.900 ⇒ 00:45:33.900 Deepika Sethi: data, and lately, ML and AI, where you are moving, right? So, that aligned with my goal of moving into latest technology. Second was, again, I know it’s not…
225 00:45:34.100 ⇒ 00:45:41.629 Deepika Sethi: It’s somewhere in between where you are not that new to the industry, but you have spent some time, and the organization is…
226 00:45:41.660 ⇒ 00:45:58.789 Deepika Sethi: a few people, and I’m assuming it will be pretty quick, the way things have been working out for me. You guys have been pretty quick in responding, so I assume that’s the organization culture, and the agility is something that got me into, like, I need to really experience the agility now.
227 00:46:00.010 ⇒ 00:46:01.740 Robert Tseng: Yeah, I think those are…
228 00:46:01.740 ⇒ 00:46:03.819 Deepika Sethi: And delivering values quickly.
229 00:46:04.960 ⇒ 00:46:17.800 Robert Tseng: Okay, sorry, I didn’t mean to cut you off. I think the volume just cut off for a little bit, but I did hear the last, like, few words. So, yeah, I mean, I just want to kind of respond to a couple things you pointed out. Yeah, I think it’s interesting.
230 00:46:18.350 ⇒ 00:46:30.920 Robert Tseng: yeah, we don’t really talk to that many people that kind of come from the enterprise world that want to work here, and so, I mean, I’m definitely open to kind of your perspective there, and, you know, we just want to share, like, you know, the…
231 00:46:31.050 ⇒ 00:46:34.230 Robert Tseng: the way that we do things at Brain Forge, like, we are really, kind of.
232 00:46:34.230 ⇒ 00:46:58.879 Robert Tseng: pushing the edge of, like, what we can use in terms of tooling. I guess everyone here is kind of staffed on clients, and so the expectation, you know, for the role, at least, that we’re trying to fill, that you’re interviewing for, as far as, like, where we slot you in seniority, I think it’s a separate question, but, definitely a good chunk of your time would be built on clients, and so kind of where we fit you in in our
233 00:46:58.880 ⇒ 00:47:02.649 Robert Tseng: Our current client mix is a big factor for, like, where, you know.
234 00:47:02.650 ⇒ 00:47:26.060 Robert Tseng: the timing of when we would bring you on. And then I think there’s also just, like, a need to, like, want to learn how to do things the Brainforge way, I guess, which is constantly evolving, but, you know, like, every single person on the team is using cursor, pushing… pushing code, making… making commits, even the non-engineering roles. And so, we’re really trying to, like, get everybody to,
235 00:47:26.060 ⇒ 00:47:33.739 Robert Tseng: you know, be AI-enabled here, and I think it’s great when people come in with industry experience and particular
236 00:47:33.740 ⇒ 00:47:56.290 Robert Tseng: yeah, like, you have a particular strength, and that’s going to really give you a level of polish in what you can deliver with the acceleration that we provide from the tooling side. So, I think those are interesting, kind of, things that stick out to me about, kind of, your background, and yeah, I think that’s… I just wanted to give you that feedback here while we have you on the call.
237 00:47:57.820 ⇒ 00:47:59.029 Deepika Sethi: That’s good to know.
238 00:47:59.400 ⇒ 00:48:00.050 Robert Tseng: Yeah.
239 00:48:00.480 ⇒ 00:48:10.589 Robert Tseng: Yeah, I guess, you know, Greg, Amber, feel free to jump in any time, but, you know, otherwise, like, I think my… my gut is, you know, basically telling me, like.
240 00:48:10.590 ⇒ 00:48:34.900 Robert Tseng: Hey, look, like, Deepika, we have, like, a few… we’re, like, kind of… we’re staffed on our enterprise client side right now, or, like, our long-term clients. We were actually thinking to have somebody come in and do, like, an earlier stage client, like, in a particular… in a new service line. So, like, for example, Greg comes from the product analytics world, he’s an expert there, and so, like, he’s kind of owning and building that service line
241 00:48:34.900 ⇒ 00:48:36.520 Robert Tseng: with us.
242 00:48:36.530 ⇒ 00:48:55.199 Robert Tseng: I don’t really know what that looks like for, you know, coming out of this conversation. I don’t know, like, what your… if there is a… if I see you leading a service line, like, today, it feels like there is a better fit when there’s, like, a enterprise client that we are kind of bringing in, which
243 00:48:55.200 ⇒ 00:49:19.850 Robert Tseng: I mean, we have a bunch in the pipeline, like, we’re taught… we’re in the fintech world, in the banking world, and so maybe, I think a good next step would just be to keep you in the loop on, like, some of the leads that we’re talking to. And, you know, if that’s a… if that’s interesting to you, that you want to actually be staff of those clients, then I feel like we could introduce you to those people, and it’s basically, once those deals sign, we would be able to… we would be able to
244 00:49:19.850 ⇒ 00:49:24.100 Robert Tseng: bring you on. So, I think that’s kind of, like, the flow of how things work.
245 00:49:24.100 ⇒ 00:49:25.780 Robert Tseng: We don’t really get to, like.
246 00:49:25.780 ⇒ 00:49:45.090 Robert Tseng: hire, like, you know, 6 months in advance of a deal. We kind of have to do it as the deal flow comes. So, I mean, I just, you know, what do you… what do you think of that as, like, is that a fair assessment of kind of where… you know, obviously we only spent 30 minutes together, but, of kind of where we’re at, and, like, is that something you’d be interested in staying in the loop for?
247 00:49:45.800 ⇒ 00:50:02.620 Deepika Sethi: Yeah, I think that makes sense, but obviously I get 30 minutes are not enough to really come up with the final event, but I think, yeah, that makes sense. I do understand my majority of experience has only been with enterprise clients, and haven’t really worked in a startup environment or so.
248 00:50:02.670 ⇒ 00:50:12.230 Deepika Sethi: And anyhow, I get eligible in the next few months, so I think, in two months, two more months, so I think that makes sense for me to equally, that makes equal sense for me, yeah.
249 00:50:12.720 ⇒ 00:50:25.219 Robert Tseng: Okay, great. Really appreciate you being, like, kind of flexible with… with this. I know… I don’t know if you’ve ever done an interview like this before. We totally pivoted from where we were going. I felt like within the first 10 minutes, I was like, she is…
250 00:50:25.220 ⇒ 00:50:34.579 Robert Tseng: too senior for, like, this… for this role that we were… we were looking for. So, yeah, I mean, I think we appreciate the effort you put, and, you know, I do want to…
251 00:50:34.610 ⇒ 00:50:47.790 Robert Tseng: I kind of keep… keep you in mind for, like, you are knocking on the doors of, of pines that I feel like would be a good fit for your skill set. So, you know, I think that’s… that’s kind of where I see things headed for me.
252 00:50:49.510 ⇒ 00:50:51.920 Deepika Sethi: No, I think, that makes sense, so…
253 00:50:52.040 ⇒ 00:51:06.500 Deepika Sethi: And I’m pretty… I totally understand that I am in a new market, and I do have to learn a lot of new things, so I have to be open. And that’s the mindset I started looking at, you know, all the job opportunities, so I totally understand that.
254 00:51:07.390 ⇒ 00:51:13.770 Robert Tseng: Great, yeah, I mean, I think maybe the last thing that, you know, while we’re kind of queuing up, like, what we can do to kind of keep you in the loop.
255 00:51:13.920 ⇒ 00:51:21.919 Robert Tseng: I think on your side, I know we’ve seen your resume, you’ve heard of Bloom, we’ve done this exercise,
256 00:51:22.170 ⇒ 00:51:30.050 Robert Tseng: Yeah, I guess, like, if there is, like, a particular type of client that you’d be interested in, I’d like to just
257 00:51:30.200 ⇒ 00:51:37.559 Robert Tseng: be able to, you know, when I’m talking to, like, Lone Star, I mean, big, 33 branches in Texas, I’m gonna talk to them later, like, tomorrow.
258 00:51:37.700 ⇒ 00:51:50.869 Robert Tseng: you know, if… I know you have some fintech background, but if I’m like, hey, you know, we have a… we have a team of people who have been in this industry for a while, like, I’d love to be able to kind of, like, slide you in there as, like, a…
259 00:51:50.890 ⇒ 00:52:10.260 Robert Tseng: part of, like, the bench that we’re putting together for them. That’s where my head is at, because we kind of need that level of validation on, like, they’ll vet the team and everything, so I want to kind of actually, like, see how I can incorporate you into the pitches, if that’s something you’re open to, for us to really kind of help,
260 00:52:10.260 ⇒ 00:52:18.840 Robert Tseng: like, kind of get in on some of these deals, like, I’d love some assistance on, like, how do you think I could better position your role on the team there?
261 00:52:20.790 ⇒ 00:52:25.970 Deepika Sethi: I would love to, like, really support the team, and as I mentioned, it would be really helpful if.
262 00:52:25.970 ⇒ 00:52:27.670 Robert Tseng: Is that me? Did I get cut off?
263 00:52:27.800 ⇒ 00:52:28.360 Deepika Sethi: buddy?
264 00:52:29.310 ⇒ 00:52:30.440 Deepika Sethi: Is it better now?
265 00:52:30.670 ⇒ 00:52:31.640 Robert Tseng: No, no, sorry.
266 00:52:31.640 ⇒ 00:52:32.060 Greg Stoutenburg: view.
267 00:52:33.850 ⇒ 00:52:37.889 Robert Tseng: My bad, I accidentally unplugged my headphone while I was, like, kind of talking.
268 00:52:38.440 ⇒ 00:52:49.509 Deepika Sethi: No problem. I was just mentioning that, obviously, coming with an experience, does give… make me a little biased toward the, ensuring that my prior experience is being used, I think,
269 00:52:49.510 ⇒ 00:52:50.040 Robert Tseng: Yeah.
270 00:52:50.040 ⇒ 00:53:01.290 Deepika Sethi: love to, like, be able to help wherever I can with that experience, and would really love to understand how you are really pitching off your products to those clients.
271 00:53:02.420 ⇒ 00:53:05.909 Robert Tseng: Okay, cool. Yeah, then I think that’s a good next step.
272 00:53:06.140 ⇒ 00:53:12.670 Robert Tseng: Yeah, I think that we don’t… we don’t have to… that’s all… that’s all I got. Anything else from the team?
273 00:53:15.180 ⇒ 00:53:15.989 Greg Stoutenburg: Go for it, Amber.
274 00:53:16.860 ⇒ 00:53:23.630 Amber Lin: No, not really for me, but I appreciate this interview, it was very insightful for me as well.
275 00:53:25.330 ⇒ 00:53:29.870 Greg Stoutenburg: Yeah, thanks. Thanks, Ibika. Great analysis, really love the case study. Nice work.
276 00:53:30.070 ⇒ 00:53:30.850 Deepika Sethi: Thank you.
277 00:53:31.450 ⇒ 00:53:35.829 Robert Tseng: Yeah. Okay, well, thanks for your time, and yeah, hope to be in touch.
278 00:53:36.240 ⇒ 00:53:37.340 Deepika Sethi: Sure, thank you.
279 00:53:38.420 ⇒ 00:53:39.410 Greg Stoutenburg: Yeah, thanks, bye.
280 00:53:39.790 ⇒ 00:53:40.660 Deepika Sethi: That is.