Meeting Title: Brainforge Final Interview Date: 2026-04-01 Meeting participants: Sai Sindhura Poosarla, Amber Lin, Robert Tseng, Greg Stoutenburg
WEBVTT
1 00:04:39.010 ⇒ 00:04:40.069 Amber Lin: Hi there!
2 00:04:41.050 ⇒ 00:04:43.120 Sai Sindhura Poosarla: Here. Hi, Amber.
3 00:04:43.370 ⇒ 00:04:45.770 Amber Lin: Hi! I don’t think we’ve talked before.
4 00:04:46.050 ⇒ 00:04:51.019 Sai Sindhura Poosarla: Yeah, I don’t, I don’t. Yeah, so, yeah, nice meeting you.
5 00:04:51.020 ⇒ 00:05:02.979 Amber Lin: Robert and, Greg will be here very soon. They’re still on a call. I think you’ve talked to both of them before, or just… just Greg?
6 00:05:03.350 ⇒ 00:05:10.450 Sai Sindhura Poosarla: No, I think I spoke with, Utam, and I did, speak with,
7 00:05:11.080 ⇒ 00:05:15.320 Sai Sindhura Poosarla: Someone else on your team might… but it was… it’s not, Greg or robot.
8 00:05:15.320 ⇒ 00:05:35.469 Amber Lin: Gotcha, okay. I remember talking with our HR folks, and I think we put you on the data engineer or data analytics engineer route, and so thank you for going through and redoing the exercise, and we’re very excited to have you here today.
9 00:05:35.930 ⇒ 00:05:40.859 Sai Sindhura Poosarla: Yeah, no worries, I totally understand. Thank you so much for your, time, man.
10 00:05:41.180 ⇒ 00:05:42.780 Sai Sindhura Poosarla: You know, considering my profile.
11 00:05:42.970 ⇒ 00:05:43.730 Sai Sindhura Poosarla: It’s a pleasure.
12 00:05:43.830 ⇒ 00:05:46.709 Amber Lin: Oops. Where are you based in right now?
13 00:05:47.270 ⇒ 00:05:50.130 Sai Sindhura Poosarla: So I’m based out of, Seattle.
14 00:05:50.370 ⇒ 00:05:52.010 Amber Lin: Oh, wow, okay.
15 00:05:53.270 ⇒ 00:05:56.700 Amber Lin: We’re in the same time zone, I’m in LA, so I’m just…
16 00:05:56.700 ⇒ 00:05:57.849 Sai Sindhura Poosarla: Oh, that’s nice.
17 00:05:57.850 ⇒ 00:05:59.030 Amber Lin: same side.
18 00:05:59.210 ⇒ 00:06:01.849 Amber Lin: Yeah, how long have you been in Seattle?
19 00:06:03.200 ⇒ 00:06:07.199 Sai Sindhura Poosarla: I would say, for now, it’s been, like, around, 8 years.
20 00:06:07.550 ⇒ 00:06:08.660 Amber Lin: Oh, wow.
21 00:06:09.450 ⇒ 00:06:10.599 Greg Stoutenburg: Hey Si, nice to meet you.
22 00:06:11.100 ⇒ 00:06:11.560 Sai Sindhura Poosarla: Hey, hi.
23 00:06:11.560 ⇒ 00:06:13.270 Amber Lin: Hi, Greg. Hey, Robert.
24 00:06:13.270 ⇒ 00:06:17.419 Robert Tseng: Sorry, we were… Greg and I were on a call that ran a little long. Yeah.
25 00:06:17.420 ⇒ 00:06:18.920 Sai Sindhura Poosarla: No, it’s higher, but…
26 00:06:21.540 ⇒ 00:06:24.139 Robert Tseng: Oh yeah, not to catch you guys off, you were, I guess, assuming.
27 00:06:24.140 ⇒ 00:06:25.650 Amber Lin: No, we were deciding.
28 00:06:27.060 ⇒ 00:06:30.980 Amber Lin: That is in Seattle, so we’re in the same time zone.
29 00:06:31.450 ⇒ 00:06:32.460 Robert Tseng: Nice.
30 00:06:34.390 ⇒ 00:06:46.599 Sai Sindhura Poosarla: Yeah, just a quick thing that, I have my camera on the monitor, whereas, like, I’m looking in the Zoom call in my, you know, the PC, so that’s the reason it looks like I’m looking down, but, like.
31 00:06:46.830 ⇒ 00:06:48.369 Sai Sindhura Poosarla: Because I see the faces here.
32 00:06:48.800 ⇒ 00:06:51.699 Sai Sindhura Poosarla: Like, if I look here and talk, I’m with.
33 00:06:51.700 ⇒ 00:06:52.070 Greg Stoutenburg: Right.
34 00:06:52.070 ⇒ 00:06:54.529 Sai Sindhura Poosarla: So, it’s just letting you know.
35 00:06:55.000 ⇒ 00:07:07.170 Greg Stoutenburg: problem. I’ve thought about buying one of those, like, wand cameras. Have you seen those things? Yeah. And then, like, when you’re on a call, you just move it down. So when you’re looking at yourself, as we all do, you’d look like you’re looking at the other person. Pretty clever.
36 00:07:07.750 ⇒ 00:07:14.939 Sai Sindhura Poosarla: Yeah, I mean, yeah, unfortunately, I just have this, like, the static camera that I need to keep, I just say, oh, okay, move here, move there.
37 00:07:15.630 ⇒ 00:07:16.190 Greg Stoutenburg: Yup.
38 00:07:17.860 ⇒ 00:07:36.159 Robert Tseng: Cool, well, I guess we can jump into it. We don’t have too much time. So, yeah, so I know you have, like, a presentation that you wanted to walk us through. I’ll just kind of give you a sense of the, like, structure so you can, yeah, we’re not expecting you to present for the whole time. Ideally, we kind of keep it contained to, you know, 10 to 15 minutes.
39 00:07:36.160 ⇒ 00:07:52.829 Robert Tseng: We’ll ask some questions for the case study. We really want to save at least half the time to, yeah, just have a chat with you more about, kind of, the role. I think both Amber and Greg have kind of chatted with you at this point. So, yeah, we want to put, you know, share more scenarios of what it’s actually like being
40 00:07:52.830 ⇒ 00:07:59.620 Robert Tseng: being, at Brainforge and the role that you’re interviewing for, and then give you some time to ask some questions.
41 00:08:00.380 ⇒ 00:08:00.820 Sai Sindhura Poosarla: Yep.
42 00:08:00.820 ⇒ 00:08:04.930 Robert Tseng: Does that sound okay? Okay, yeah, cool. Then, yeah, we’ll let… we’ll kind of, you know.
43 00:08:04.930 ⇒ 00:08:24.929 Robert Tseng: I don’t know if this was emphasized in the deck, but, you know, just kind of present to us as if we were the client that you’re presenting to, and yeah, I mean, people may jump in, so you kind of have to… if you get interrupted, don’t take it personally, just, yeah, I think that’s… just trying to simulate, kind of, real, real-world scenario here.
44 00:08:25.060 ⇒ 00:08:25.770 Robert Tseng: Yeah.
45 00:08:26.200 ⇒ 00:08:32.840 Sai Sindhura Poosarla: For sure, yeah, that totally makes sense. Thank you so much for the quick overview. So, I can,
46 00:08:33.059 ⇒ 00:08:36.179 Sai Sindhura Poosarla: Start sharing my screen.
47 00:08:36.900 ⇒ 00:08:40.789 Sai Sindhura Poosarla: Okay, gimme… A minute.
48 00:08:41.090 ⇒ 00:08:41.820 Sai Sindhura Poosarla: Okay.
49 00:08:44.340 ⇒ 00:08:48.539 Sai Sindhura Poosarla: So, I believe, can everyone see my screen?
50 00:08:50.010 ⇒ 00:08:50.640 Greg Stoutenburg: Yes.
51 00:08:51.180 ⇒ 00:08:55.710 Sai Sindhura Poosarla: Okay, perfect,
52 00:08:57.000 ⇒ 00:09:09.739 Sai Sindhura Poosarla: So, the assessment deck that I have shared with you is this copy, but, I know we want to jump into that, but I also have, like, an AI-enhanced version, so I was trying to stick to
53 00:09:10.210 ⇒ 00:09:23.899 Sai Sindhura Poosarla: the template that was provided by the Brainforge, but the fun fact is, when I do this slide deck and I have asked AI to enhance and make it, like, beautiful and pleasing, I did get a different version that is
54 00:09:24.280 ⇒ 00:09:31.899 Sai Sindhura Poosarla: yeah, that is in a different way, so yeah, we can… we can do the comparison later, like, how AI is, helping stuff, but
55 00:09:32.490 ⇒ 00:09:38.010 Sai Sindhura Poosarla: Yeah, quickly, so on the presentation side,
56 00:09:39.320 ⇒ 00:09:43.340 Sai Sindhura Poosarla: A quick background is the dataset that was,
57 00:09:43.780 ⇒ 00:09:56.090 Sai Sindhura Poosarla: provided has, like, 4 stores, and it’s all about, the comparison between these stores, and also, like, how Dashmart is doing well than the other stores.
58 00:09:56.690 ⇒ 00:10:02.100 Sai Sindhura Poosarla: So, before we get in real quick, so I wanted to provide a quick background.
59 00:10:02.560 ⇒ 00:10:10.690 Sai Sindhura Poosarla: And then, I’ll talk about the proposals, like, what recommendations I have from this… Dataset.
60 00:10:10.800 ⇒ 00:10:19.769 Sai Sindhura Poosarla: A little bit about the timeline. So, with these recommendations, what are the deliverables, what could I implement, and how does the timeline look?
61 00:10:20.040 ⇒ 00:10:27.789 Sai Sindhura Poosarla: And at the end, I can touch upon, like, the insights and the data analysis that actually led to these recommendations.
62 00:10:31.330 ⇒ 00:10:32.920 Sai Sindhura Poosarla: Does it sound good? Okay.
63 00:10:34.800 ⇒ 00:10:37.759 Sai Sindhura Poosarla: Seems like no questions so far, so okay, I’ll just proceed.
64 00:10:39.910 ⇒ 00:10:40.710 Sai Sindhura Poosarla: Yeah.
65 00:10:42.130 ⇒ 00:10:46.739 Sai Sindhura Poosarla: Yeah, the overall, yeah, first I can start… get started with the background.
66 00:10:46.980 ⇒ 00:11:02.670 Sai Sindhura Poosarla: So this, like, the dataset that was provided is, like, for one month, and it has, like, 4 stores, around, like, 60K items delivered, and there are about, like, 30K delivery IDs, so if I dedupe at the delivery ID, there are about, 30,000 rows.
67 00:11:03.220 ⇒ 00:11:21.599 Sai Sindhura Poosarla: And, with the price per item column that was provided in the dataset, if I kind of extrapolate the revenue from these four stores for one month, I would see it’s about, like, 300K monthly revenue, again, like, extrapolating through an year, it’s, like, you know, around 1.2 to 1.5 million revenue.
68 00:11:24.390 ⇒ 00:11:33.140 Sai Sindhura Poosarla: Based on this, dataset, I have, I would say, like, 3 proposals, or, like, 3 recommendations.
69 00:11:33.330 ⇒ 00:11:37.199 Sai Sindhura Poosarla: So, as I stated earlier, like, there are 4 stores.
70 00:11:37.430 ⇒ 00:11:47.320 Sai Sindhura Poosarla: And, and the analysis was performed at the store level. So the top 3 recommendations that I have is the grocery 1 store.
71 00:11:47.480 ⇒ 00:11:51.360 Sai Sindhura Poosarla: Has a higher, you know, item missing rate.
72 00:11:52.600 ⇒ 00:12:03.390 Sai Sindhura Poosarla: So, the proposal one is kind of to focus on the grocery 1 store, and look up on the inventory, and enhance the inventory accuracy of the proposal… of the grocery store one.
73 00:12:04.780 ⇒ 00:12:10.720 Sai Sindhura Poosarla: And my second recommendation is… Out of all these missing items.
74 00:12:11.210 ⇒ 00:12:19.070 Sai Sindhura Poosarla: There are specific product categories where, you know, we have seen these items are being missed repeatedly.
75 00:12:19.240 ⇒ 00:12:21.980 Sai Sindhura Poosarla: So there are, like, so many occurrences of that.
76 00:12:22.170 ⇒ 00:12:39.709 Sai Sindhura Poosarla: So, if we could focus on what is happening with those missing items, understand the top SKUs that is contributing to the missing rate, and also, like, see if we need to work on the demand forecasting, or, like, substitution logic.
77 00:12:39.710 ⇒ 00:12:44.280 Sai Sindhura Poosarla: So, the second recommendation is more about the product category.
78 00:12:45.830 ⇒ 00:12:49.460 Sai Sindhura Poosarla: And, moving on to the third recommendation.
79 00:12:49.850 ⇒ 00:12:55.379 Sai Sindhura Poosarla: Out of all the grocery stores, I think the grocery store, too.
80 00:12:56.320 ⇒ 00:13:08.210 Sai Sindhura Poosarla: it has, like, the highest D2R distance, like, highest average drive to reach the store for the Dasher. And, it also has highest cancellation rate.
81 00:13:08.500 ⇒ 00:13:12.270 Sai Sindhura Poosarla: So, there is an opportunity for us, to
82 00:13:12.430 ⇒ 00:13:23.959 Sai Sindhura Poosarla: you know, strategize the footprint of, grocery store to understand if the Dasher, allocation or, like, resource allocation is accurate or not.
83 00:13:24.830 ⇒ 00:13:32.110 Sai Sindhura Poosarla: So, yeah, these are, like, the high, high-level, like, three strategies or recommendations that I have for.
84 00:13:32.620 ⇒ 00:13:33.660 Sai Sindhura Poosarla: this dataset.
85 00:13:35.430 ⇒ 00:13:36.950 Sai Sindhura Poosarla: Any questions?
86 00:13:37.290 ⇒ 00:13:41.400 Sai Sindhura Poosarla: As of Okay.
87 00:13:41.400 ⇒ 00:13:42.210 Greg Stoutenburg: Not yet.
88 00:13:42.510 ⇒ 00:13:43.290 Sai Sindhura Poosarla: Okay.
89 00:13:43.480 ⇒ 00:13:44.830 Sai Sindhura Poosarla: Let me proceed.
90 00:13:48.530 ⇒ 00:14:06.919 Sai Sindhura Poosarla: Okay, so again, coming to the, timeline, so for the grocery store one, where I mentioned that there is, like, a lot of opportunity with the grocery store one, so the proposal is, the deliverables are to establish, like, a scorecard for the grocery store one.
91 00:14:08.450 ⇒ 00:14:16.160 Sai Sindhura Poosarla: So, where we’ll monitor, kind of, a couple of core metrics and see how the grocery store one is performing against
92 00:14:16.330 ⇒ 00:14:27.199 Sai Sindhura Poosarla: the Dash Mart, or all the other stores. So, establishing that reliability scorecard dashboard, and making sure, you know, we monitor the performance of these stores.
93 00:14:28.670 ⇒ 00:14:37.389 Sai Sindhura Poosarla: For the proposal, too, again, like, I kind of mentioned, like, couple of product categories are having, like, the higher missing rate.
94 00:14:37.490 ⇒ 00:14:45.050 Sai Sindhura Poosarla: So, pretty much, you know, doing the category audit, like, understanding the substitution rules, and,
95 00:14:45.150 ⇒ 00:14:55.139 Sai Sindhura Poosarla: Pretty much around the enhancing the product category, so that is the second, proposal, and we could, have the action items for that.
96 00:14:56.470 ⇒ 00:15:03.020 Sai Sindhura Poosarla: And the third, one is pretty much, like, understanding the, you know, dashers.
97 00:15:03.050 ⇒ 00:15:07.490 Sai Sindhura Poosarla: average D2R and cancellation rate. So, if we…
98 00:15:07.500 ⇒ 00:15:17.009 Sai Sindhura Poosarla: have higher a dasher to re… like, higher distance for the dasher to reach the store? Like, is there anything else that, I could improve there?
99 00:15:17.010 ⇒ 00:15:28.009 Sai Sindhura Poosarla: Like, if we could, like, recruit more dashers around grocery too, and that could help in, you know, reducing the distance for the dasher to reach the store.
100 00:15:31.210 ⇒ 00:15:39.160 Sai Sindhura Poosarla: Again, in the scorecard, for, in order to monitor the performance of the stores.
101 00:15:39.170 ⇒ 00:15:58.469 Sai Sindhura Poosarla: A couple of metrics that, I thought about is… the first one is, delivery missing rate, which is pretty straightforward, like, the deliveries, the total number of deliveries that have any missing item, divided by the total deliveries. So this will give us, like, how many successful deliveries we were able to do.
102 00:15:58.810 ⇒ 00:16:06.000 Sai Sindhura Poosarla: The second one is unresolved missing grade, which is pretty much, like, you know,
103 00:16:06.060 ⇒ 00:16:24.370 Sai Sindhura Poosarla: Out of all the items ordered, how many are missing, and how many of the missing items, they got substitutes. Even after the substitution, if there are any unresolved missing items, so that percentage is also… could be a good indicator as well.
104 00:16:25.250 ⇒ 00:16:36.860 Sai Sindhura Poosarla: And the other thing is about the customer issue rate, which is more about, like, you know, the number of items or the number of orders that were issued by the customer as missing.
105 00:16:37.500 ⇒ 00:16:46.959 Sai Sindhura Poosarla: Then it’s, like, cancellation rate and the late 20 rate, like, the number of orders that got canceled, number of orders that reached later than 20 minutes, for the promised time.
106 00:16:49.360 ⇒ 00:16:56.139 Sai Sindhura Poosarla: And just going into, like, why are we doing this analysis and how this will help you is…
107 00:16:56.400 ⇒ 00:17:06.150 Sai Sindhura Poosarla: Currently, I think the way we, the way the missing rate in the stores is treated as, like, a generic problem that happens in the majority of the grocery stores.
108 00:17:06.150 ⇒ 00:17:22.580 Sai Sindhura Poosarla: So after we, you know, do this analysis, establish these dashboards or scorecard, I think we are finding, specific problems and also tailored solutions and, you know, targeted recommendations and business strategies.
109 00:17:22.579 ⇒ 00:17:25.149 Sai Sindhura Poosarla: That could help enhance the business.
110 00:17:25.150 ⇒ 00:17:35.009 Sai Sindhura Poosarla: instead of, you know, treating it as, like, oh yeah, we have missing rate, and missing rate is pretty common, I think we are, tackling it more as, like, an opportunity here.
111 00:17:39.120 ⇒ 00:17:42.730 Sai Sindhura Poosarla: And, proceeding, to… Sorry.
112 00:17:43.060 ⇒ 00:17:47.119 Robert Tseng: Hey, can we… can we just jump back to your 3 proposal slide? Slide 6?
113 00:17:47.770 ⇒ 00:17:49.770 Sai Sindhura Poosarla: Slide… 6, okay.
114 00:17:50.030 ⇒ 00:17:51.050 Sai Sindhura Poosarla: Yeah. Perfect.
115 00:17:51.770 ⇒ 00:18:00.670 Robert Tseng: Yeah, I just want to spend some time here. So, yeah, I guess, like, for the inventory accuracy, what do you, like, what’s… what’s causing, what’s causing the inventory accuracy here?
116 00:18:03.500 ⇒ 00:18:08.889 Sai Sindhura Poosarla: So… which… the first one, the Proposal Inventory Act.
117 00:18:08.890 ⇒ 00:18:11.660 Robert Tseng: Yeah, what are, what are the, what are the accuracy issues?
118 00:18:12.140 ⇒ 00:18:27.379 Sai Sindhura Poosarla: Yeah, so if we see, like, the grocery 1 store has a lot of, you know, missing items, and even after the substitution, the unresolved missing items is pretty high. So I could, dive into the data, actually.
119 00:18:27.580 ⇒ 00:18:31.790 Sai Sindhura Poosarla: So, if I see, like, the top SKUs with unresolved missing items.
120 00:18:35.230 ⇒ 00:18:45.259 Sai Sindhura Poosarla: So these are the top SKUs that are, unresolved missing items. So these are the items that they are reported, like, they don’t have the substitute, and those are reported missing.
121 00:18:45.260 ⇒ 00:18:46.339 Sai Sindhura Poosarla: So, these are…
122 00:18:46.340 ⇒ 00:19:10.020 Sai Sindhura Poosarla: So that means that you need to, you know, there is an opportunity for grocery 1 to do a better demand forecasting. So as you see, like, you know, like, there are a lot of drinks products that are in demand, that are needed by the customers for the grocery store 1, and what we realized is those are mostly missing, and there are no substitute as well. So we could really focus on, you know, drink rates.
123 00:19:10.290 ⇒ 00:19:17.330 Sai Sindhura Poosarla: And, going… Yeah, I can jump…
124 00:19:17.530 ⇒ 00:19:37.360 Sai Sindhura Poosarla: Real quick, so I was trying to get into the data, but we can jump real quick. So, when I was saying the missing rate by store, so I was calculating at the order level. So, for the dashboard, the missing rate is very less, so 0.9 percentage. And for grocery 1, it is 53.7%, so more than half of the orders are
125 00:19:37.530 ⇒ 00:19:47.089 Sai Sindhura Poosarla: are having one or the other missing items. So, and grocery store, grocery 1 contributes, to a significant portion in the revenue.
126 00:19:47.260 ⇒ 00:19:56.359 Sai Sindhura Poosarla: So, you know, enhancing these missing items and, like, tackling this missing rate would help contribute to the increase in the revenue as well.
127 00:19:57.890 ⇒ 00:20:08.589 Robert Tseng: So you’re saying if we address this, I mean, the impact is that you would increase 1% of revenue, so if you resolve… if Grocery 1 orders this moving percentage, drops to zero.
128 00:20:08.820 ⇒ 00:20:13.180 Robert Tseng: Yes. Then the overall revenue would increase by 1%. That’s what you’re… that’s what you’re saying?
129 00:20:13.500 ⇒ 00:20:26.649 Sai Sindhura Poosarla: Yeah, the overall revenue would increase by 1%, and again, the numbers might look small, because it’s just one sample data, one month’s sample data, but on the expiration, I feel like there is, like, a lot of potential for revenue increase.
130 00:20:26.900 ⇒ 00:20:35.190 Sai Sindhura Poosarla: And the other reason I wanted to concentrate on Grass Street 1 is because it’s also, like, it also has, like, large amount of order share.
131 00:20:35.390 ⇒ 00:20:40.720 Sai Sindhura Poosarla: After the dashboard, so that’s the best opportunity for us to work on.
132 00:20:40.890 ⇒ 00:20:56.039 Sai Sindhura Poosarla: Whereas grocery 3, the data is very less. I don’t know, like, if there is, like, not a lot of customers are ordering for that. And grocery 2 has, like, footprint issues, so the best recommendation is to focus on grocery 1 for now.
133 00:20:56.400 ⇒ 00:20:59.919 Sai Sindhura Poosarla: And work on, you know, reducing this missing rate.
134 00:21:01.660 ⇒ 00:21:20.399 Robert Tseng: Got it. I’m actually not sure how you got to your 1%, like, increase in revenue. I’m curious because, like, opportunity sizing is something that clients ask a lot about. Like, we can give them with a bunch of different recommendations, but clients want to know, like, what’s the impact of my business? So, I would like to understand how you got there more.
135 00:21:24.350 ⇒ 00:21:27.139 Sai Sindhura Poosarla: Yeah, this was a result in the,
136 00:21:27.260 ⇒ 00:21:34.260 Sai Sindhura Poosarla: So, the one percentage, the way I got is, like, the grocery store… one second… Yeah.
137 00:21:44.760 ⇒ 00:21:51.949 Sai Sindhura Poosarla: Yeah, so the grocery store, currently, it is contributing, to the 30% of overall revenue.
138 00:21:52.290 ⇒ 00:22:05.339 Sai Sindhura Poosarla: And grocery store 1 has 5 percentage of missing rate. So 5% of 30% is 1% increase in revenue. So that’s how I, you know, extrapolated.
139 00:22:05.340 ⇒ 00:22:05.710 Robert Tseng: I see.
140 00:22:05.710 ⇒ 00:22:07.110 Sai Sindhura Poosarla: this opportunity cost.
141 00:22:07.810 ⇒ 00:22:12.420 Robert Tseng: Okay, cool, no, interesting, yeah, interesting way to calculate, yeah.
142 00:22:12.580 ⇒ 00:22:13.350 Robert Tseng: Thanks.
143 00:22:13.510 ⇒ 00:22:24.240 Sai Sindhura Poosarla: I mean, again, it seems small, but that’s a good opportunity. Like, when we actually extrapolate a couple of years, or, like, monthly data, I think this is a great opportunity for a glossary one.
144 00:22:24.800 ⇒ 00:22:25.590 Sai Sindhura Poosarla: Okay.
145 00:22:26.630 ⇒ 00:22:39.319 Robert Tseng: I’ll probably ask one more question on… let’s just pick a different one. Like, Proposal 3, yes, tell me about this, like, footprint intervention. I think I was… I didn’t really understand what you were referring to when you first showed the slide.
146 00:22:39.610 ⇒ 00:22:46.520 Sai Sindhura Poosarla: Yeah, that makes sense. So when I meant, like, the footprint, intervention, so let me… Ugh.
147 00:22:48.150 ⇒ 00:22:53.090 Sai Sindhura Poosarla: Yeah, so buy all the stores, so if I take… buy, grocery 1,
148 00:22:53.230 ⇒ 00:23:05.549 Sai Sindhura Poosarla: So, the grocery 2, for example, here. Average drive to reach the store, the average D to R column. So, this is pretty high for the grocery store to orders.
149 00:23:05.550 ⇒ 00:23:13.440 Sai Sindhura Poosarla: So, the way I’m interpreting this is, like, it is taking longer for our dashers to reach the grocery 2 store.
150 00:23:13.870 ⇒ 00:23:30.090 Sai Sindhura Poosarla: So, is there any opportunity for us, to recruit, more dashers that are nearby the store, or also understand why it is taking longer for them to reach? Whereas, if you see for the other grocery stores, right, it is, like, almost like half.
151 00:23:30.090 ⇒ 00:23:42.909 Sai Sindhura Poosarla: You know, 4 minutes and, like, 3 minutes or something. The grocery 3, I would say, like, the data that we have is very limited, so there is, like, a lot that… there is not a lot that we could, really interpret from that.
152 00:23:42.970 ⇒ 00:24:00.850 Sai Sindhura Poosarla: Whereas, grocery, too, has good amount of data, and it is taking longer for dashers to reach there. So, if we could enhance that, increase board number of dashers, that will ultimately, you know, promote more customers to order, more customer satisfaction.
153 00:24:00.850 ⇒ 00:24:06.340 Sai Sindhura Poosarla: And there will not be, late orders, so I was thinking it from that point of view.
154 00:24:11.260 ⇒ 00:24:11.750 Robert Tseng: Definitely.
155 00:24:11.750 ⇒ 00:24:14.580 Sai Sindhura Poosarla: I’m, I’m happy to… yeah.
156 00:24:15.520 ⇒ 00:24:23.120 Robert Tseng: Yeah, no, that… that makes… that makes sense. I’m curious, like, how you… how you assess… so you have your average CLAT, sure, and then…
157 00:24:23.230 ⇒ 00:24:31.339 Robert Tseng: How are you setting this threshold? Like, what would be… like, if you were to drive an improvement here, like, what… what’s feasible… what’s feasible?
158 00:24:31.870 ⇒ 00:24:35.590 Robert Tseng: Can you ask Make it less than 20 minutes late, or, like, kind of…
159 00:24:35.720 ⇒ 00:24:48.309 Robert Tseng: I guess I understand this, like, average analysis, but I’m trying… I want to try to get more towards, like, a recommendation of, like, you’re going to improve this by, like, how… what… what do you think is realistic?
160 00:24:49.270 ⇒ 00:25:01.399 Sai Sindhura Poosarla: Yeah, the one thing is, like, it’s, again, like, the recommendation is to, you know, recruit more dashes that are nearby the store, so that we have, more,
161 00:25:01.680 ⇒ 00:25:12.070 Sai Sindhura Poosarla: You know, more dashers getting from the stores and picking up, and also it… the probability of, you know, cancellation rate or the probability of late delivery would be less.
162 00:25:12.090 ⇒ 00:25:29.129 Sai Sindhura Poosarla: So, it’s just that if I’m a Dasher, I’m going to the store, and I’m taking long for me to reach the store, and from there, if I am… if I have to deliver to a customer, so the overall time, I want to reduce that overall time taken.
163 00:25:29.770 ⇒ 00:25:39.350 Sai Sindhura Poosarla: and if the customer… and if the deliveries are faster, like, dashers will also be more interested to, you know, pick up the orders. They will do faster delivery, and also, like.
164 00:25:39.480 ⇒ 00:25:56.060 Sai Sindhura Poosarla: it’ll ultimately result in more customers ordering in our stores, and it’s like a flywheel cycle, right? You know, you have… dashers are happy, customers are happy, you get more orders, the stores are happy. Ultimately, like, the three-way,
165 00:25:56.190 ⇒ 00:25:59.520 Sai Sindhura Poosarla: Like, three-sided business place, like, making sure everyone is happy.
166 00:26:02.320 ⇒ 00:26:04.220 Robert Tseng: Yeah. Okay, I got you.
167 00:26:04.440 ⇒ 00:26:17.419 Robert Tseng: Yeah, so I just want to be mindful of time. I think we can probably end the presentation here, and then, yeah, I just want to give you some feedback, and, yeah, can you give you some time to ask some questions?
168 00:26:17.600 ⇒ 00:26:30.240 Robert Tseng: So, yeah, I know that, like, throughout this interview process, you kind of interviewed for a couple roles. One was, like, more on the data engineering side, and then we kind of, like, had you take the strategy exercise, so appreciate you being flexible here.
169 00:26:30.250 ⇒ 00:26:43.759 Robert Tseng: Yeah, I actually thought, like, your takeaways were pretty solid. I think you have good analytics skills. I didn’t even realize I designed the exercise so that there’s only a 1% revenue lift in fixing the grocery lawn accuracy, so…
170 00:26:43.760 ⇒ 00:26:56.390 Robert Tseng: I feel like I should probably change it to make it a little bit more substantial. So yeah, I think, like, your ability to spot, like, kind of the opportunity… like, how to size the opportunity was… was interesting. I think that was… that was… that was strong.
171 00:26:56.500 ⇒ 00:27:14.770 Robert Tseng: I think on the proposal 3 side, I actually thought you know the data very well, too. You had good ideas in terms of, like, you know, how are you actually going to get, kind of, what are the inputs to this flywheel system? I think it was hard to pick up from the presentation, and it only made more sense once we discussed it.
172 00:27:14.770 ⇒ 00:27:15.350 Sai Sindhura Poosarla: So, yeah.
173 00:27:15.350 ⇒ 00:27:27.150 Robert Tseng: I mean, I think, those are… I mean, I know we didn’t listen to your full presentation, but I think those were… just kind of seeing you react to those two kind of questions, I think I… I feel like I got a sense of…
174 00:27:27.150 ⇒ 00:27:30.789 Robert Tseng: kind of… I mean, I think you know, you know, you understand the data very well.
175 00:27:30.790 ⇒ 00:27:48.029 Robert Tseng: And then I think the… yeah, I mean, I think your analytic skills are strong. I think the presentation on the slides is not, it’s not… it’s not super clear. So, yeah, I think, like, the roles that we’re interviewing for now might have shifted at this point from, like, when you were
176 00:27:48.030 ⇒ 00:27:56.559 Robert Tseng: When you first applied, like, we were only really trying to, like, fill the senior role, and so I think when Greg and I are, like, trying to evaluate
177 00:27:56.560 ⇒ 00:28:14.979 Robert Tseng: at this point, it’s like, do we feel like this candidate could be put in front of a client today and be able to communicate their findings very clearly? I feel like you’re not at the, at least from what we’ve seen, the presentation isn’t at the senior level.
178 00:28:14.980 ⇒ 00:28:23.849 Robert Tseng: But I think you’re a really strong analyst, and I just… I just don’t feel like we would be able to make a decision on moving forward with, like, a mid-level analyst right now.
179 00:28:23.860 ⇒ 00:28:32.120 Robert Tseng: And this is all just, like, seniority in terms of, like, Brainforge world. I’m sure, like, other companies do it differently. But yeah, we’re…
180 00:28:32.120 ⇒ 00:28:37.379 Robert Tseng: You know, the only two kind of, like, senior, like, analyst roles we’re kind of looking for right now are
181 00:28:37.380 ⇒ 00:28:53.770 Robert Tseng: ones that can either be put in front of clients, like, talking to VP-level, C-level people right away, or people who have, like, a very specific domain specialty that become, like, a service leader to, to, basically set the standards for how a certain work stream is done.
182 00:28:54.050 ⇒ 00:28:55.400 Robert Tseng: Which…
183 00:28:55.430 ⇒ 00:29:07.179 Robert Tseng: I… I don’t… I mean, at least maybe we just didn’t get that from your previous interviews, or we didn’t… we didn’t ask it more directly, so I’d like to maybe just give you a chance to kind of share more about, like.
184 00:29:07.180 ⇒ 00:29:21.709 Robert Tseng: hey, like, is there a specific domain that you, like, are… like, that you consider, like, yourself specialized in? I think that would probably be where I would like to head, for the rest… for the rest of our conversation.
185 00:29:22.740 ⇒ 00:29:37.060 Sai Sindhura Poosarla: Yeah, I think that, totally makes sense, and I… yeah, I honestly appreciate your, feedback, you know, immediately feedback, you know, instead of, giving me waiting, so I totally appreciate the candidness, and,
186 00:29:37.180 ⇒ 00:29:52.170 Sai Sindhura Poosarla: depending on the domain expertise, so I could say, like, recent, my experience has been a lot in the workforce analytics space, so the HR, like, the, you know, attrition, retention, you know, headcount world.
187 00:29:52.220 ⇒ 00:30:08.900 Sai Sindhura Poosarla: But earlier to this, I did, have experience in product analytics quite a bit, you know, when I was working back in, user testing. So, for the user testing, I did a lot of product analytics, like product usage metrics, like.
188 00:30:08.940 ⇒ 00:30:21.540 Sai Sindhura Poosarla: you know, launching A-B testing, or, you know, enhancing the products and features, and tracking the usage of customers. So, I do have that experience, so product analytics.
189 00:30:21.680 ⇒ 00:30:30.780 Sai Sindhura Poosarla: And, earlier to that, I did do a little bit of, you know, financial analytics, like the credit card,
190 00:30:30.850 ⇒ 00:30:41.470 Sai Sindhura Poosarla: policy launch and the credit card users and, you know, their usage trends. So, yeah, I would, kind of say myself, like, domain expertise in
191 00:30:41.470 ⇒ 00:30:51.010 Sai Sindhura Poosarla: probably two things, product analytics, and the other one is, like, the people analytics space. So, yeah, there I definitely have seen, like, good amount of data sets and.
192 00:30:51.620 ⇒ 00:30:52.400 Robert Tseng: Got it.
193 00:30:52.400 ⇒ 00:31:01.229 Greg Stoutenburg: Yeah, product analytics is how I got here, so, yeah, what’s your, what’s your, what’s your background like? Can you, tell me about a project, or…
194 00:31:01.520 ⇒ 00:31:04.360 Greg Stoutenburg: Some fun… some fun product analytics stuff you’ve worked on?
195 00:31:05.110 ⇒ 00:31:10.320 Sai Sindhura Poosarla: For the product analytics, yeah, for sure. So…
196 00:31:10.490 ⇒ 00:31:18.000 Sai Sindhura Poosarla: I would say not recently, because, like, recent my experience has always been in the attrition and retention and, you know, people analytics space.
197 00:31:18.060 ⇒ 00:31:33.870 Sai Sindhura Poosarla: But, you know, while I was working back at, user testing, so the way I used to do product analytics is I used to closely work, with the data science team, and for all the new product features that were launched, and it’s a…
198 00:31:34.230 ⇒ 00:31:50.879 Sai Sindhura Poosarla: It’s a product that has, like, that has a lot of customers, so we used to do the usage metrics of, you know, how well is this feature being utilized, what is, like, how is the clicks and all the journey of the customer in a product.
199 00:31:50.980 ⇒ 00:31:59.180 Sai Sindhura Poosarla: And understanding, like, if a specific feature is really important or not, or, like, how can we better enhance the product?
200 00:31:59.850 ⇒ 00:32:08.880 Greg Stoutenburg: Yeah, so just to clarify, you worked for user testing, helping user testing improve their onboarding and activation and retention and stuff like that?
201 00:32:08.880 ⇒ 00:32:10.510 Sai Sindhura Poosarla: Exactly, yes. Yeah, yeah.
202 00:32:10.510 ⇒ 00:32:11.000 Greg Stoutenburg: Okay, cool.
203 00:32:11.410 ⇒ 00:32:29.770 Greg Stoutenburg: Yeah, okay, cool, yeah, that’s, that’s… I mean, that’s… that’s fun. When you were… I mean, what does it turn out? I’m just curious, like, what were… what were some of the sticky events that someone would have to perform, when they sign up for user testing to end up being successful users of the product?
204 00:32:30.810 ⇒ 00:32:41.620 Sai Sindhura Poosarla: Yeah, for sure, like, you know, when you sign up for the user testing, first of all, like, there… it has been, like, a… you go through, like, an onboarding process of how to use the product.
205 00:32:41.800 ⇒ 00:32:46.289 Sai Sindhura Poosarla: Then you launch, like, a, you know, you launch, like, a test.
206 00:32:46.400 ⇒ 00:32:50.060 Sai Sindhura Poosarla: So, the test is, like, you kind of create the test, so…
207 00:32:50.230 ⇒ 00:33:04.670 Sai Sindhura Poosarla: I would say, like, okay, this is my web page, I need, you know, 10 people, you know, 2 from United States, 2 from different country, and you can also specify the sample of people that you would want them to test your product.
208 00:33:04.750 ⇒ 00:33:18.560 Sai Sindhura Poosarla: And you could easily specify, like, you know, I am looking for feedback on, you know, login page. I’m looking for the feedback on enhanced recommender system that I have developed in my order page. So…
209 00:33:18.680 ⇒ 00:33:34.590 Sai Sindhura Poosarla: Yeah, so you could clearly get descriptive with the test that you want, and depending upon the number of people you want, there is, like, a group of, you know, existing panel members that people would be chosen for this test.
210 00:33:34.720 ⇒ 00:33:43.959 Sai Sindhura Poosarla: And every panel member would go in and do the test. Test is nothing but they do the steps, and they give, like, a live, recorded feedback session.
211 00:33:44.250 ⇒ 00:33:55.669 Sai Sindhura Poosarla: which, you know, ultimately the customer, or, like, the product managers, or the UX researchers, they could, you know, review and understand what are the pain points.
212 00:33:55.800 ⇒ 00:33:57.999 Sai Sindhura Poosarla: What is working well with the customers?
213 00:33:58.410 ⇒ 00:34:05.320 Greg Stoutenburg: So, the users who, the users who would go in and create some customer segments.
214 00:34:05.760 ⇒ 00:34:11.959 Greg Stoutenburg: those are the ones who tended to stick around. Am I understanding that correctly? Like, the product managers and the designers, when they.
215 00:34:11.969 ⇒ 00:34:12.599 Sai Sindhura Poosarla: Yes.
216 00:34:12.600 ⇒ 00:34:18.159 Greg Stoutenburg: Create an account for user testing, and they would create segments to reach out and do some experiments with. That’s when you… that’s when you got them?
217 00:34:18.500 ⇒ 00:34:19.399 Sai Sindhura Poosarla: Yes, yes.
218 00:34:19.539 ⇒ 00:34:21.650 Sai Sindhura Poosarla: Yeah.
219 00:34:21.840 ⇒ 00:34:39.990 Greg Stoutenburg: Okay, when you made that discovery, what… what was the impact? Like, just, you know, real briefly, like, what was some of the follow-on work that made you, you know, come up with some experiments to run, or some changes to make to the product to, you know, optimize for that, and then what was the result of it?
220 00:34:40.889 ⇒ 00:34:43.469 Sai Sindhura Poosarla: Yeah, that’s a great question. So…
221 00:34:43.969 ⇒ 00:34:55.069 Sai Sindhura Poosarla: So again, like, stepping in the toes of customers. So I’m a customer, I launched a new website, I’m going and seeing, like, okay, I wanted 10 people to give feedback, and then the initial product was like, okay, I,
222 00:34:55.069 ⇒ 00:35:06.439 Sai Sindhura Poosarla: once the feedback is given, it would be shipped to the customer. Okay, so here are your 10 feedback videos. You could take advantage of the feedback and all, like, there’s a customized email or notification sent to the customer.
223 00:35:06.529 ⇒ 00:35:10.579 Sai Sindhura Poosarla: The customer would spend, like, you know, half an hour going through every video.
224 00:35:10.739 ⇒ 00:35:18.719 Sai Sindhura Poosarla: So, instead of that, I proposed, and I worked with the data science team, that how about we categorize these videos?
225 00:35:18.909 ⇒ 00:35:30.319 Sai Sindhura Poosarla: You know, based on the sentiment. The first video has positive sentiment, second video has negative, third video is neutral, and group those videos in terms of sentiment.
226 00:35:31.189 ⇒ 00:35:38.179 Sai Sindhura Poosarla: In that way, like, okay, it kind of reduced the time for the customer to take decision.
227 00:35:38.489 ⇒ 00:35:39.399 Sai Sindhura Poosarla: So…
228 00:35:39.409 ⇒ 00:35:56.169 Sai Sindhura Poosarla: now I want to know, oh, I know what is working well, but I really want to know what are the pain points. I just go to the negative feedback videos and, just focus on them, instead of, you know, seeing the neutral videos where, you know, it’s like, oh yeah, I think it’s working well, it’s great, or something like that.
229 00:35:56.179 ⇒ 00:36:02.559 Sai Sindhura Poosarla: So, I kind of worked to enhance the product, and that product feature got launched.
230 00:36:02.639 ⇒ 00:36:05.699 Sai Sindhura Poosarla: And when it was launched, I think we really…
231 00:36:05.829 ⇒ 00:36:23.969 Sai Sindhura Poosarla: Received a lot of, you know, appreciation about this feature, and again, in terms of data and how this metric was measured, the time spent by the customer to review the videos and ultimately take decision reduced.
232 00:36:24.069 ⇒ 00:36:43.969 Sai Sindhura Poosarla: And that is, like, a data way of proving it, other than that, you know, when we were having, like, conversation, you know, a couple of customers, like, not me, because I don’t interact with those customers, it’s mostly, like, the customer success managers or the account managers, we did hear testimonials from the customer saying that they’ll love this feature.
233 00:36:44.530 ⇒ 00:37:01.450 Greg Stoutenburg: Cool, yeah, nice. Yeah, good project, nice work. That’s interesting stuff. Cool. Yeah, I, you know, I work on product analytics here now, talked to a product analytics customer just an hour ago, that’s how we relate, and, are pitching some others right now, so that’s, that is a work stream that we care about.
234 00:37:02.420 ⇒ 00:37:04.199 Sai Sindhura Poosarla: For sure, yeah, makes sense.
235 00:37:04.730 ⇒ 00:37:23.559 Robert Tseng: Cool. Yeah, so I think we’re gonna… we’ll just wrap it up here, Sy. Yeah, I think, like, as far as… yeah, so I don’t think we’ll do, like, the senior, kind of, client-facing, like, associate route. I think I’d be interested in, kind of, staying in touch with you on the product analytics work as that opens up.
236 00:37:23.700 ⇒ 00:37:42.039 Robert Tseng: HR analytics is not really, like, in our book of business right now, so, I mean, even though I would like to break into that, we just currently don’t have that type of client, so I don’t think there’d be a project to staff you on there. So yeah, I think that’s kind of how… I mean, I think that’s kind of where we’re at right now.
237 00:37:42.040 ⇒ 00:37:54.800 Robert Tseng: So we’d love to stay in touch, like, I think you’re a strong candidate, like, yeah, really strong analyst, so, yeah, if there’s, you know, any… anything that we can be helpful with in our… I think you’re connected with a few of us on…
238 00:37:54.860 ⇒ 00:38:13.319 Robert Tseng: on email and LinkedIn, like, happy to make intros, but, I think for Brainforge right now, like, I… I think we don’t have, an opportunity to plug you into right away. So, you know, also don’t want to kind of keep… lead you on there as well. So, yeah, I guess…
239 00:38:13.320 ⇒ 00:38:22.339 Robert Tseng: happy to take any last questions from your side, but otherwise, I think that’s kind of, I think, the way forward.
240 00:38:23.380 ⇒ 00:38:25.540 Sai Sindhura Poosarla: Yeah, I think that’s definitely a great,
241 00:38:25.710 ⇒ 00:38:41.569 Sai Sindhura Poosarla: you know, again, I’m saying, like, I really appreciate the honesty and, you know, immediate feedback, instead of, you know, keeping on waiting. But, yeah, the only thing is, like, yeah, I can stay connected, and if you see any other opportunity, I’m always happy to, you know, help.
242 00:38:42.590 ⇒ 00:38:47.860 Robert Tseng: Okay, great. Yeah, thank you so much for your time, Sy, and, yeah, hope to stay in touch.
243 00:38:48.490 ⇒ 00:38:50.349 Sai Sindhura Poosarla: Sure. Thank you so much.
244 00:38:50.350 ⇒ 00:38:50.680 Greg Stoutenburg: up.