Meeting Title: Brainforge x Eden Monthly Review Date: 2025-12-23 Meeting participants: Sezim Zhenishbekova, Uttam Kumaran, Robert Tseng
WEBVTT
1 00:02:08.830 ⇒ 00:02:09.780 Uttam Kumaran: -Oh.
2 00:02:10.800 ⇒ 00:02:12.270 Uttam Kumaran: Sorry for the delay.
3 00:02:12.700 ⇒ 00:02:13.829 Sezim Zhenishbekova: No worries.
4 00:02:14.220 ⇒ 00:02:15.260 Uttam Kumaran: How’s everything?
5 00:02:15.980 ⇒ 00:02:20.059 Sezim Zhenishbekova: Good… getting ready for my flight tomorrow, basically.
6 00:02:20.060 ⇒ 00:02:21.850 Uttam Kumaran: Oh, really? How long? Yeah.
7 00:02:21.930 ⇒ 00:02:29.739 Sezim Zhenishbekova: It’s only until 5th of, January. 5th of January, I’m going to Michigan, yeah, for a week.
8 00:02:29.740 ⇒ 00:02:31.309 Uttam Kumaran: Oh, nice, okay, okay.
9 00:02:31.680 ⇒ 00:02:33.690 Uttam Kumaran: Yeah. What’s in Michigan?
10 00:02:33.690 ⇒ 00:02:38.779 Sezim Zhenishbekova: So, when I was in high school, I used to be an exchange student there, so I know.
11 00:02:38.780 ⇒ 00:02:39.330 Uttam Kumaran: Oh, just…
12 00:02:39.330 ⇒ 00:02:44.990 Sezim Zhenishbekova: I still kept in touch with them, so every holiday, they just invite me if I’m around.
13 00:02:44.990 ⇒ 00:02:46.159 Uttam Kumaran: Oh, great, okay.
14 00:02:47.130 ⇒ 00:02:47.680 Sezim Zhenishbekova: Yeah.
15 00:02:47.680 ⇒ 00:02:48.570 Uttam Kumaran: Wonderful.
16 00:02:49.430 ⇒ 00:02:50.060 Sezim Zhenishbekova: Yeah, and my.
17 00:02:50.060 ⇒ 00:02:51.060 Uttam Kumaran: Are you.
18 00:02:51.060 ⇒ 00:02:52.560 Sezim Zhenishbekova: Please.
19 00:02:52.700 ⇒ 00:02:55.399 Uttam Kumaran: Oh, okay, you’re staying with your host family there, or gonna go meet.
20 00:02:55.400 ⇒ 00:03:01.809 Sezim Zhenishbekova: Yeah, yeah, yeah, yeah, yeah, I’m gonna stay with them. They’re just, like, my extended family of some sort, so that’s why.
21 00:03:01.810 ⇒ 00:03:02.400 Uttam Kumaran: Oh, okay.
22 00:03:02.400 ⇒ 00:03:05.410 Sezim Zhenishbekova: My room is… no one touches it.
23 00:03:05.410 ⇒ 00:03:07.450 Uttam Kumaran: Really? Oh, nice.
24 00:03:07.450 ⇒ 00:03:09.650 Sezim Zhenishbekova: Yeah, I got lucky with them.
25 00:03:09.650 ⇒ 00:03:10.440 Uttam Kumaran: recognize.
26 00:03:11.140 ⇒ 00:03:12.090 Sezim Zhenishbekova: They’re hiring.
27 00:03:12.690 ⇒ 00:03:13.490 Robert Tseng: It says in.
28 00:03:13.730 ⇒ 00:03:14.580 Sezim Zhenishbekova: Yeah.
29 00:03:14.760 ⇒ 00:03:16.040 Robert Tseng: Are you in Michigan now?
30 00:03:16.040 ⇒ 00:03:17.650 Sezim Zhenishbekova: No, I’m leaving tomorrow.
31 00:03:17.650 ⇒ 00:03:18.760 Robert Tseng: Oh, tomorrow, okay.
32 00:03:18.760 ⇒ 00:03:26.790 Sezim Zhenishbekova: Yeah, I hope tomorrow’s not gonna rain or snow, or nothing, but a big thing will happen, because it’s snowing, it was snowing in the morning.
33 00:03:27.120 ⇒ 00:03:28.670 Sezim Zhenishbekova: Which is good, I guess.
34 00:03:28.670 ⇒ 00:03:30.140 Robert Tseng: In New York, yeah, yeah.
35 00:03:30.980 ⇒ 00:03:31.790 Sezim Zhenishbekova: Yeah.
36 00:03:32.060 ⇒ 00:03:35.320 Sezim Zhenishbekova: How’s San Francisco? Were you in California?
37 00:03:35.320 ⇒ 00:03:47.369 Robert Tseng: Yeah, yeah, we met over in California over the weekend. Gabe and Luke were here as well, so it was good. Tom’s back at home now, and I’ll be here until the 28th. I go back to New York, so…
38 00:03:47.370 ⇒ 00:03:53.499 Sezim Zhenishbekova: Nice. Yeah. Go Rams! Poor them, Kevin.
39 00:03:53.500 ⇒ 00:03:54.500 Robert Tseng: Oh, sure, yeah.
40 00:03:54.500 ⇒ 00:03:56.150 Sezim Zhenishbekova: Thanks.
41 00:03:56.730 ⇒ 00:04:02.940 Sezim Zhenishbekova: Yes, so today’s purpose of the call is to get a review, right?
42 00:04:02.940 ⇒ 00:04:13.170 Robert Tseng: Yeah, yeah, we just want to, you know, you’ve been here for almost a month now, right? I know it’s kind of been off and on in terms of hours, and kind of, we had a bit of a holiday from Thanksgiving, but…
43 00:04:13.170 ⇒ 00:04:13.690 Sezim Zhenishbekova: Yeah.
44 00:04:13.690 ⇒ 00:04:18.749 Robert Tseng: Yeah, I guess, like, I’ll catch Utam up to speed a bit, because I’ve been meeting with you more regularly.
45 00:04:18.810 ⇒ 00:04:37.330 Robert Tseng: So I gave you this kind of, like, JD that we’re supposed to be co-building together. I don’t think I’ve added notes to it since we last talked, but this more or less kind of captures, like, our expectations for, like, what this role is, and then we spend a little time discussing kind of how you’ve kind of checked, you know, whether or not we’re… what…
46 00:04:37.330 ⇒ 00:04:42.089 Robert Tseng: How you’re meeting these, or kind of where… what areas are still of a stretch for you.
47 00:04:42.150 ⇒ 00:04:46.100 Robert Tseng: I think in general, like, you know, this part of just, like.
48 00:04:46.100 ⇒ 00:05:10.380 Robert Tseng: taking structured and unstructured data and being able to, like, produce analysis from it, I think you can do that. I think you’re… obviously, your… the output… what you delivered for… for Insomnia was great. Like, I think the… you’re… clearly, you’re, you know, on the Excel side and spreadsheets, like, you understand that better than most of our team, so I think that’s kind of where you… where your… where your strength is in terms of, like, financial modeling and being able to.
49 00:05:10.380 ⇒ 00:05:16.229 Robert Tseng: You know, kind of organize, data in, like, that type of operational reporting.
50 00:05:16.670 ⇒ 00:05:38.160 Robert Tseng: And then on this side, like, yeah, it seems like, you know, we’ve gotten good feedback so far. I’ve talked to Jonah from Eden, like, he thinks that you’re, yeah, you’re a good, good, good analyst partner, and so, yeah, it seems like stakeholders, you know, appreciate kind of your presence there, and then, obviously, as you’ve plugged into our, like.
51 00:05:38.160 ⇒ 00:05:45.909 Robert Tseng: stand-ups, and you’ve been doing some pair programming with Henry, and a couple, and Amber. Yeah, I mean, they’ve both kind of…
52 00:05:45.910 ⇒ 00:05:52.380 Robert Tseng: given good feedback for you so far, so I think you understand how to plug into our system now.
53 00:05:52.380 ⇒ 00:06:08.039 Robert Tseng: obviously, first time working with a data engineering team, you’re still, like, kind of unsure of, like, when you should go get the data yourself versus when you should be asking our team. And I think that’s something you’ll pick up over time. But, yeah, I think this is generally… you can see, like, how it…
54 00:06:08.040 ⇒ 00:06:12.849 Robert Tseng: You know, what it’s like fitting into a pod for us when we’re, like, working with a client, right?
55 00:06:12.990 ⇒ 00:06:30.479 Robert Tseng: And then, like, kind of areas that, like, I feel like are less, that are more of a stretch for you right now, like, yeah, the presentation side, we haven’t put you in terms of, like, giving a client-facing, presentation. You’ve contributed to DEX, I don’t know if you’ve
56 00:06:30.480 ⇒ 00:06:33.819 Robert Tseng: Like, I… I don’t really know if you’ve, been able to…
57 00:06:33.820 ⇒ 00:06:45.730 Robert Tseng: produce those decks from scratch on your own yet. I think you’re usually just contributing to one or two slides here and there. So yeah, I think there’s definitely some room to grow and, like, being able to
58 00:06:47.110 ⇒ 00:06:57.290 Robert Tseng: button up everything and, like, put it in front of the client. You’ve done, like, the Loom video, which is helpful when you’re walking through a spreadsheet model, but just, like, more of that, like, client-ready, like.
59 00:06:57.520 ⇒ 00:07:11.490 Robert Tseng: you know, decks, memos, whatever, whatever that is. Like, I think that’s definitely, like, kind of, you know, what we need to work towards so that we feel confident putting you in front of clients and just being able to just talk to them directly.
60 00:07:11.940 ⇒ 00:07:17.419 Robert Tseng: On the, yeah, continuing to know how to work with data engineering, like.
61 00:07:17.810 ⇒ 00:07:40.350 Robert Tseng: functioning as, like, a PM, where you’re… if there’s missing data and you need something, being able to work with the engineers, bring those requirements to them, you know, not just, like, waiting for me to kind of bring them up and stand up, so that you’re helping scope out new projects, right? If you’ve observed the dynamic between Zoran and, like, the data engineers, like with Ashwini, Zoran, like, gets
62 00:07:40.350 ⇒ 00:07:52.800 Robert Tseng: he’s working on some things with the client. If he needs something, he’s going to the data engineers directly, he’s creating tickets for them, like, he knows how to, like, work with them now so that he gets what he needs for his work. So, I don’t… yeah, I think…
63 00:07:52.920 ⇒ 00:08:00.479 Robert Tseng: I haven’t seen you kind of, like, ask for anything that new from them yet, so, you know, I think that’s just something that, you know, an area that we would want to see you grow in.
64 00:08:00.940 ⇒ 00:08:17.359 Robert Tseng: And then, like, really, just, this is more kind of higher level, like, how do you, like, opportunity size, like, the things that you’re doing? Right now, it’s more kind of, the project is kind of defined for you, given to you, but, like, you know, with something more open-ended.
65 00:08:17.360 ⇒ 00:08:22.599 Robert Tseng: like, I see you struggling when you’re, like, building forecast models of, like.
66 00:08:22.600 ⇒ 00:08:42.310 Robert Tseng: okay, you can model multiple scenarios, but, like, having an opinion of, like, this is the one that we should go with, because, these… I trust these assumptions more, this is the better opportunity, like, you’re not really in those conversations yet, but, like, ultimately, that’s… that’s what it… I think it will… that’s what it will require, to, like.
67 00:08:42.309 ⇒ 00:09:06.069 Robert Tseng: to raise the level of confidence from our clients to trust you as a consultant, right? So, we wear multiple hats with them, we’re their engineering team, we’re their analysts, we’re also, like, their thought partner as a consultant, too. So, obviously, like, you know, you’re not the main, like, lead on a client yet, but eventually, we would like you to be able to, you know, for example, with Amber on Insomnia.
68 00:09:06.440 ⇒ 00:09:10.300 Robert Tseng: She is telling their lifecycle marketer
69 00:09:10.300 ⇒ 00:09:34.500 Robert Tseng: this is the campaign that you should run. Go run it, or go run this, and, like, make sure it gets on her roadmap, she executes it, and then, we’re able to come back and see the results and be like, great, the campaign that Amber recommended to them, performs at 3 times better than what they’re… what they normally see, and that’s… that should be a win for us. So, like, that type of, like, you know, kind of really going end-to-end.
70 00:09:34.500 ⇒ 00:09:50.739 Robert Tseng: like, you know, obviously, you know, it takes time to get there, but that’s what we would like to see as well. So, that’s really what I tried to summarize with this, and we can change the text, but would you, would you… I mean, does that all kind of sound like kind of what we discussed last time in a bit?
71 00:09:50.740 ⇒ 00:09:53.329 Sezim Zhenishbekova: Yes, yes, I agree with everything.
72 00:09:53.790 ⇒ 00:09:54.150 Robert Tseng: Yeah.
73 00:09:54.820 ⇒ 00:10:00.709 Robert Tseng: Yeah, UTOM, so that’s… that’s kind of what we’ve… what I’ve… what I’ve tried to put together with her before.
74 00:10:00.710 ⇒ 00:10:01.180 Uttam Kumaran: Okay.
75 00:10:01.180 ⇒ 00:10:02.570 Robert Tseng: Yeah, so…
76 00:10:02.640 ⇒ 00:10:26.830 Robert Tseng: Yeah, I think, you know, ultimately, you know, one month’s not a long time, especially as a part-timer, so we’re kind of having to go off of, like, you know, do we see the potential? And, like, I think we do see the potential, like, you do have some, like, core skills that you bring to the table as well, so we’re just trying to figure out, like, what does it look like in this next, you know, phase for you? I’d love to hear from your perspective, yeah, like, what do you… what do you… what are you hoping for when
77 00:10:26.830 ⇒ 00:10:29.429 Robert Tseng: your, with your time with Brainforge.
78 00:10:29.640 ⇒ 00:10:34.930 Robert Tseng: Yeah, like, we can go in many… we can go in different directions of, like, what we can offer you.
79 00:10:35.820 ⇒ 00:10:44.040 Sezim Zhenishbekova: In general, like, the first month was very interesting. It’s my first time working for a data engineering consulting firm that
80 00:10:44.040 ⇒ 00:11:08.279 Sezim Zhenishbekova: doesn’t only just give, like, decks and some market research results, but dives into the systems, runs queries, and backs it up with, like, solid numbers. So it was very fascinating to see so many tools that are integrated, and how they work with one another. So it took me some time to adjust, and I definitely agree with the stretch goals, that I would love to learn how to cross…
81 00:11:08.280 ⇒ 00:11:27.140 Sezim Zhenishbekova: function as data engineers, because at some point in time, I was tagging basically everyone, starting with Zachary Lindy Milada, and then Henry, because I started panicking at some point. But then, for the regarding the business model and for forecasting, I completely agree as well, because
82 00:11:27.170 ⇒ 00:11:33.270 Sezim Zhenishbekova: I’ve done, like, some forecasting, but it wasn’t… like, I think, for me, it’s…
83 00:11:33.410 ⇒ 00:11:45.360 Sezim Zhenishbekova: better to get used to the business model itself first, before making grant recommendations. That’s why, for me, I think I need more time to feel more confident about what I suggest.
84 00:11:45.600 ⇒ 00:11:48.739 Sezim Zhenishbekova: But yeah, that’s the area I would also…
85 00:11:48.740 ⇒ 00:11:56.659 Uttam Kumaran: what the work for Jonah was, like, and I, like, I have a background in, like, financial analysis, so…
86 00:11:56.660 ⇒ 00:11:57.180 Sezim Zhenishbekova: It can be asleep.
87 00:11:57.180 ⇒ 00:12:00.219 Uttam Kumaran: I suppose you want, but I’m just, like, interested in, like, what…
88 00:12:00.330 ⇒ 00:12:02.870 Uttam Kumaran: what we did for him. Yeah.
89 00:12:04.150 ⇒ 00:12:04.920 Sezim Zhenishbekova: Okay.
90 00:12:05.460 ⇒ 00:12:08.739 Sezim Zhenishbekova: With me, I’m just trying to file.
91 00:12:08.740 ⇒ 00:12:12.670 Uttam Kumaran: It’s fun, because we don’t do a lot of, like, true financial stuff, which I’m bummed about.
92 00:12:12.670 ⇒ 00:12:13.300 Sezim Zhenishbekova: Yeah.
93 00:12:13.300 ⇒ 00:12:19.350 Uttam Kumaran: Because that’s, it’s very typically rudimentary.
94 00:12:19.600 ⇒ 00:12:25.560 Uttam Kumaran: But there’s a lot of dollars involved, so yeah, I’m just curious what we actually did for him.
95 00:12:26.350 ⇒ 00:12:27.440 Sezim Zhenishbekova: Yeah, go.
96 00:12:30.670 ⇒ 00:12:31.640 Sezim Zhenishbekova: That’s crazy.
97 00:12:31.640 ⇒ 00:12:33.940 Robert Tseng: Oops, did I press the wrong button? Did you…
98 00:12:33.940 ⇒ 00:12:39.489 Sezim Zhenishbekova: No, I think I got it right. Okay, so during our call, he had… we shared
99 00:12:39.550 ⇒ 00:12:47.780 Sezim Zhenishbekova: our ideas about forecasting, we ultimately walk through with Henry, the roadmap that he has built, the first draft of it.
100 00:12:47.780 ⇒ 00:12:59.779 Sezim Zhenishbekova: And he said, like, he likes it all, he likes that we can incorporate mocker factors and also focus on something that he has already been working on. So, this project self-fund is something that he has built.
101 00:12:59.810 ⇒ 00:13:13.740 Sezim Zhenishbekova: Where he uses different, like, metrics and retention… And so this is his spreadsheet. Yes, this is his spreadsheet. He said, like, don’t… you don’t need to take it all, but just use it as an idea to bounce it off.
102 00:13:13.800 ⇒ 00:13:19.809 Sezim Zhenishbekova: Before, like, to build something for forecasting that will benefit him the most.
103 00:13:19.970 ⇒ 00:13:36.799 Sezim Zhenishbekova: So he says that, like, these numbers are, like, bogus, like, he doesn’t… he just, like, randomly assigned it by himself, and he says, like, the numbers are not accurate, but the logic is there, where he tries to get the retention rate for, the… based on the samples.
104 00:13:37.010 ⇒ 00:13:41.310 Sezim Zhenishbekova: And projects it for the months ahead. And it’s pretty messy, like…
105 00:13:42.020 ⇒ 00:13:47.840 Sezim Zhenishbekova: Yeah, he has, like, a couple working documents in several other places.
106 00:13:47.980 ⇒ 00:13:53.610 Sezim Zhenishbekova: So yeah, it took me some time to figure it out. So, and then requests from…
107 00:13:54.390 ⇒ 00:14:06.299 Sezim Zhenishbekova: Mitesh came, and then when the Mitesh came in, and it was kind of similar, it actually helped me to better understand the project before jumping into Johan’s work.
108 00:14:06.600 ⇒ 00:14:16.119 Sezim Zhenishbekova: Where I just, like, studied all the products that they sell, what are the statuses, like, what each status means, and why it’s been activated in the system.
109 00:14:16.120 ⇒ 00:14:29.249 Sezim Zhenishbekova: And breaking it down in more detail helped me to understand the retention cycle itself, where, through top blue, I was getting the data and breaking down the forecasting. This one is forecasting for
110 00:14:29.250 ⇒ 00:14:32.060 Sezim Zhenishbekova: It’s a trend analysis by,
111 00:14:32.060 ⇒ 00:14:40.040 Sezim Zhenishbekova: Once over change… calculating months over a month’s change, and then just applying to the number of returning orders and new orders.
112 00:14:40.420 ⇒ 00:14:47.550 Sezim Zhenishbekova: And then the same thing here, too. This is the retention cycle that Johan, Jonah was using.
113 00:14:48.230 ⇒ 00:14:59.339 Sezim Zhenishbekova: So I just averaged it out and applied to each new order, but, Joha, he wants to apply it specifically to, revenue.
114 00:15:02.570 ⇒ 00:15:05.030 Sezim Zhenishbekova: And by customer’s profit.
115 00:15:05.780 ⇒ 00:15:07.660 Uttam Kumaran: So what was, like, the final output here?
116 00:15:09.160 ⇒ 00:15:17.919 Sezim Zhenishbekova: So, currently, we… so my main goal when I had a call with him was to build a forecasting model where
117 00:15:18.340 ⇒ 00:15:39.900 Sezim Zhenishbekova: they will understand, like, where is the company going, and, like, how much revenue they’re making, and how much they’re spending. So, to have that type of approximate estimation on how everything is running. That was the grand goal, and then he shared this information, and then we jumped in into… mostly into the logistical part of things, operations side of things.
118 00:15:40.620 ⇒ 00:15:49.919 Uttam Kumaran: So I guess that’s what I just want to, like, kind of poke in on, is, like, where… where is this gonna end up? Like, is this an Excel model? Is this something in Tableau? Because I guess so far, what you’ve showed me is all
119 00:15:50.280 ⇒ 00:15:54.700 Uttam Kumaran: that, like, either Mitesh or Jonah has done. Like, I’m trying to see some of the work that you did.
120 00:15:55.160 ⇒ 00:16:03.130 Sezim Zhenishbekova: So this one is completely done. This whole work is my work. This one is completely my work. Mitesh didn’t do anything, he just sent.
121 00:16:03.130 ⇒ 00:16:03.600 Uttam Kumaran: Okay, okay.
122 00:16:03.600 ⇒ 00:16:12.200 Sezim Zhenishbekova: And I built all these views. Great, great, great. Henry helped me out, with, like, recommendations and suggestions. It was very helpful.
123 00:16:12.210 ⇒ 00:16:24.579 Sezim Zhenishbekova: Yeah, that’s pretty much it. From the Yona side, I didn’t jump in, because I’m still studying it, and, like, I know it’s much better than, like, a couple weeks ago, but, like, this is Mitesh’s request… And what was the feedback from Italian.
124 00:16:24.580 ⇒ 00:16:25.440 Uttam Kumaran: on this.
125 00:16:25.870 ⇒ 00:16:40.259 Sezim Zhenishbekova: So he asked a different question. Instead of checking the refill numbers for the specific drug, now we’re asking how many orders, returning orders we’re gonna have.
126 00:16:40.460 ⇒ 00:16:49.119 Sezim Zhenishbekova: new orders. So that was the new request, and we have built the new request, and today or tomorrow, we’re sending him our updates.
127 00:16:50.000 ⇒ 00:16:57.010 Uttam Kumaran: Okay. Yeah, I guess probably two pieces for me is, one, like, I would like to see how much of this you can do in a business.
128 00:16:57.010 ⇒ 00:16:57.330 Sezim Zhenishbekova: Apologies.
129 00:16:57.330 ⇒ 00:17:02.030 Uttam Kumaran: useful, like Tableau. Like, I think as a data team.
130 00:17:02.210 ⇒ 00:17:05.369 Uttam Kumaran: Like, we want to avoid doing what
131 00:17:05.420 ⇒ 00:17:21.389 Uttam Kumaran: we tell other teams to do, which not to do, which is pull stuff into spreadsheets, and sort of now, we’re left with a bunch of stale data sitting, right? So one thing to think about is, like, how can we actually do all this in a tableau, and all of what you showed
132 00:17:21.390 ⇒ 00:17:33.809 Uttam Kumaran: for… seems like it’s all possible. So that’s where I would totally like you to see you use the tools that we have to report on data, to use for your analysis as well. It’s always okay to do ad hoc things.
133 00:17:33.850 ⇒ 00:17:38.889 Uttam Kumaran: But, like, this is something that the team is gonna use forever, so this is where you’ll.
134 00:17:38.890 ⇒ 00:17:39.380 Sezim Zhenishbekova: It’ll work.
135 00:17:39.380 ⇒ 00:17:43.810 Uttam Kumaran: with the data team to understand, hey, I have a vision for a report, I need.
136 00:17:43.810 ⇒ 00:17:44.260 Sezim Zhenishbekova: Thank you guys.
137 00:17:44.260 ⇒ 00:17:54.870 Uttam Kumaran: to help me build a model that acts in this way. Here’s the Excel version of what I built. Help me just bring that into Tableau. That’s a perfectly okay ask for that team.
138 00:17:54.870 ⇒ 00:17:55.390 Sezim Zhenishbekova: Yeah.
139 00:17:55.390 ⇒ 00:18:02.620 Uttam Kumaran: Right? And then eventually, you’ll learn… walk with them as they do it, and then you’ll learn, okay, actually, like, that’s… doing that is not that hard.
140 00:18:02.620 ⇒ 00:18:02.940 Sezim Zhenishbekova: I can…
141 00:18:02.940 ⇒ 00:18:05.979 Uttam Kumaran: probably do it, or I could maybe deliver them the SQL query first.
142 00:18:06.010 ⇒ 00:18:20.930 Uttam Kumaran: See, that’s another thing. Second thing is for… to think about, just, like, how do you create an asset that’s easy to flow to Robert, and for Robert to share with leadership, right? Currently, even walking through that, it was just not… it wasn’t clear for me
143 00:18:20.930 ⇒ 00:18:32.499 Uttam Kumaran: like, what’s the output here, right? So what I’m used to looking at on the… for financial models, like, okay, if you sell me an Excel workbook with, like, clean tabs, and it’s very clear, like, what the goal of this… this sort of workbook is, okay.
144 00:18:32.500 ⇒ 00:18:32.960 Sezim Zhenishbekova: I’ve expanded.
145 00:18:32.960 ⇒ 00:18:35.720 Uttam Kumaran: But for me, it’s like, there’s a bunch of views.
146 00:18:36.050 ⇒ 00:18:42.539 Uttam Kumaran: it’s still, like, it… there’s a lot of just, like, ad hoc filters and analysis, like, it’s not a… it’s not a finished
147 00:18:42.670 ⇒ 00:18:52.740 Uttam Kumaran: wrap a bow around, here’s where we’re working on this problem. You know that in, like, for Excel, like, people are gonna… especially in finance, they’re gonna care about the formatting, and they’re gonna care about it being called
148 00:18:52.740 ⇒ 00:19:04.549 Uttam Kumaran: So for me, this is where, like, for a lot of our other folks on the data side, often they’re working in SQL, so the only thing I’m looking at is whether they’re writing clean SQL and whether they’re writing effective code.
149 00:19:04.590 ⇒ 00:19:23.469 Uttam Kumaran: for you, if your world is going to be really heavy in Excel finance, you just have to have good workbook hygiene, you know? You have to really make sure that… that for me, like, if I’m the only… if I’m one of the few people in the company that can help you on financial analysis work, I have to be able to walk in that workbook and then, like, understand, like, what’s going on, right?
150 00:19:24.000 ⇒ 00:19:32.969 Uttam Kumaran: And so that’s another thing. You’re… and this is where, like, you may not get that from… our company is not heavy with financial analysts. Most of our team
151 00:19:32.970 ⇒ 00:19:45.529 Uttam Kumaran: who are doing analysis work, are doing this ad hoc stuff, and they’re producing decks, you’re truly the first person that, I think, comes from that background. So, but what I need you to do is level us up.
152 00:19:45.560 ⇒ 00:20:02.760 Uttam Kumaran: Right? It’s to really show the team what great, like, financial analysis is in Excel or in reporting, and that’s gonna really set the level for everybody else. And so for me, like, when we talk about that, like, that’s what I’m… that’s what I’m looking for.
153 00:20:02.920 ⇒ 00:20:19.069 Uttam Kumaran: You know, because we do a lot of work in Excel and things like that, but again, it just ends up all over the place, and I think… I know the skill set you have can really level that up. So that’s probably just the one thing there, but I kind of do understand where this is going. I think overall, those are my recommendations, you know, just looking.
154 00:20:19.790 ⇒ 00:20:20.650 Uttam Kumaran: worst product.
155 00:20:22.080 ⇒ 00:20:38.380 Sezim Zhenishbekova: Yeah, I completely agree. I could have done better jobs, like, writing, like, some summary of the old tables. It’s a good representation of titles, basically, to help better respond to the requests, or just explain the charts better, yeah.
156 00:20:38.380 ⇒ 00:20:38.740 Uttam Kumaran: Yeah.
157 00:20:38.740 ⇒ 00:20:44.139 Robert Tseng: Yeah, or even, like, kind of what you had made for Insomnia, I felt like was, like, a finished product, right? I kind of gave you…
158 00:20:44.140 ⇒ 00:20:45.940 Sezim Zhenishbekova: Oh, yeah, that one…
159 00:20:45.940 ⇒ 00:20:47.930 Uttam Kumaran: Saw that one once, too, yeah.
160 00:20:47.930 ⇒ 00:20:50.469 Robert Tseng: Yeah, so, like, that to me was, like, finished. Like, I felt…
161 00:20:50.470 ⇒ 00:20:50.920 Uttam Kumaran: Great competition.
162 00:20:50.920 ⇒ 00:21:08.150 Sezim Zhenishbekova: Yeah, that was completely agree. Yeah, yeah. The ad hoc one was last week, and I was going back and forth, because we were not completely understanding what Mitesh wants. So, yeah, usually I take time, like, analyzing it, and then going to design in the later stages, so I don’t need to do the double work.
163 00:21:08.570 ⇒ 00:21:14.429 Uttam Kumaran: I would do the double work. You just have to always be prepared that Robert may be in a meeting, and he has to pull that up.
164 00:21:15.310 ⇒ 00:21:16.180 Uttam Kumaran: So…
165 00:21:16.370 ⇒ 00:21:29.870 Uttam Kumaran: anything you put out into the world, assume it’s gonna get shared with the client. And so it’s up to you to just make sure that it’s elevated, and the client is not gonna ask you to make it pretty, but I’m telling you it matters. Nobody’s ever…
166 00:21:29.870 ⇒ 00:21:32.630 Sezim Zhenishbekova: Like, the font size matters.
167 00:21:32.630 ⇒ 00:21:35.569 Uttam Kumaran: Yeah, I know, but you know as much as I do when it comes to financial.
168 00:21:35.570 ⇒ 00:21:35.930 Sezim Zhenishbekova: Yeah.
169 00:21:35.930 ⇒ 00:21:53.550 Uttam Kumaran: analysis in Excel, like, those things matter, and we’re gonna start going to companies where they’re gonna be much more sticklers about it, and we just want to never get dinged for stuff like that. So the presentation aspect, I think, is gonna be big, but I saw, I think I saw the Insomnia,
170 00:21:53.550 ⇒ 00:21:54.090 Sezim Zhenishbekova: Yeah.
171 00:21:54.090 ⇒ 00:21:56.300 Uttam Kumaran: spreadsheet, and that looked great.
172 00:21:56.300 ⇒ 00:21:56.680 Sezim Zhenishbekova: Yeah.
173 00:21:56.680 ⇒ 00:22:04.039 Uttam Kumaran: So for me, like, I think those are two… I mean, I guess, thinking about that, like, what… what has been tough in the past months?
174 00:22:04.420 ⇒ 00:22:09.100 Uttam Kumaran: And, like, what has been easy? Like, you know, reflecting just on, like, the past month of work.
175 00:22:10.320 ⇒ 00:22:13.680 Sezim Zhenishbekova: So, what was hard?
176 00:22:14.140 ⇒ 00:22:20.010 Sezim Zhenishbekova: It could be anything. Onboarding, onboarding was hard, for me, specifically, because, I mean.
177 00:22:20.410 ⇒ 00:22:31.220 Sezim Zhenishbekova: I felt like I was very overwhelmed. Like, I had a big stake in every project, I felt like, and I felt very, like, huge responsibility on my shoulder, and I got to, like.
178 00:22:31.220 ⇒ 00:22:31.890 Uttam Kumaran: Change, by the way.
179 00:22:31.890 ⇒ 00:22:33.589 Sezim Zhenishbekova: That’s just… it’s gonna happen.
180 00:22:33.590 ⇒ 00:22:34.420 Uttam Kumaran: bigger, so…
181 00:22:34.420 ⇒ 00:22:35.300 Sezim Zhenishbekova: It’s not good.
182 00:22:35.300 ⇒ 00:22:36.190 Uttam Kumaran: get smaller.
183 00:22:36.190 ⇒ 00:22:41.799 Sezim Zhenishbekova: Exactly, that’s why I started freaking out and then going through different resources,
184 00:22:41.840 ⇒ 00:23:01.010 Sezim Zhenishbekova: And even if I’m not that capable of doing, like, data queries, tabloos, I just started jumping in and, like, going and trying to understand how it works, and it took way more time than I usually expected, because, yeah, like, I think that was the part of the onboarding, and yes, I went through the Notion documents, yes, there were too many… like, there were…
185 00:23:01.010 ⇒ 00:23:08.329 Sezim Zhenishbekova: so many documentation, but sometimes I had a hard time understanding what’s relevant… what’s most important that I need to stick to.
186 00:23:08.330 ⇒ 00:23:15.140 Sezim Zhenishbekova: and, like, focus on, because at some point in Insomnia, when I was making Insomnia’s scorecard, I was like.
187 00:23:15.190 ⇒ 00:23:24.789 Sezim Zhenishbekova: everywhere, and not finishing the scoreboard itself, because I was like, okay, let me do this, okay, let me have a call with Amber and Casey, because they’re actually using it, so they know the best.
188 00:23:24.790 ⇒ 00:23:25.920 Uttam Kumaran: Like, what.
189 00:23:25.920 ⇒ 00:23:26.730 Sezim Zhenishbekova: Do you think about it?
190 00:23:26.730 ⇒ 00:23:29.849 Uttam Kumaran: ability now to boil down, like, to take, for example.
191 00:23:30.260 ⇒ 00:23:35.489 Uttam Kumaran: Going… walking into a brand new client where you don’t know anything and you’re asked to do something is all we do.
192 00:23:35.490 ⇒ 00:23:37.310 Sezim Zhenishbekova: Right? So I’m not gonna…
193 00:23:37.310 ⇒ 00:23:47.510 Uttam Kumaran: I’m not gonna promise you that that’s gonna change, but how do you feel emotionally about, like, okay, you feel confident that you can start to do that? You know the resources that’s in the company, you know how to, like.
194 00:23:47.510 ⇒ 00:23:48.390 Sezim Zhenishbekova: Yeah.
195 00:23:48.390 ⇒ 00:23:49.030 Uttam Kumaran: Okay.
196 00:23:49.540 ⇒ 00:23:54.319 Sezim Zhenishbekova: So, I feel like this is a muscle that I need to work on, I guess, because, like.
197 00:23:54.440 ⇒ 00:24:04.140 Sezim Zhenishbekova: Yes, like, jumping from one industry to industry, like, being in the investment side of things, this is common when we evaluate different businesses very fast.
198 00:24:04.140 ⇒ 00:24:15.659 Sezim Zhenishbekova: And no matter, like, what kind of speed it is, so I think I can do that, but, like, when incorporating the data engineering side of things, where I also need to know where to pull what, that’s also a big struggle that’s…
199 00:24:15.660 ⇒ 00:24:21.209 Sezim Zhenishbekova: also adding additional thinking process, so I think, like, coming months is…
200 00:24:21.210 ⇒ 00:24:30.749 Sezim Zhenishbekova: that will be the big thing that I would love to work on, to see how I can incorporate different sites into my analysis, and have a bigger vision of
201 00:24:30.760 ⇒ 00:24:35.910 Sezim Zhenishbekova: what’s… What’s global, like, globally needs to be done, right?
202 00:24:36.470 ⇒ 00:24:46.959 Uttam Kumaran: Yeah, and I think as an analyst, really the way you’re gonna become more powerful is by starting to bridge, go beyond just analysis work. Like, starting to understand what is, like.
203 00:24:46.960 ⇒ 00:24:48.310 Sezim Zhenishbekova: Yeah, I agree.
204 00:24:48.310 ⇒ 00:25:05.669 Uttam Kumaran: being able to pull data on yourself, and then also continuing to build on the client presentation aspect. As you notice in the company, everybody here does, like, 2 or 3 things, like, adjacent to their core function, right? Like, them a lot is really good at modeling. Also, it’s gonna work with the client to gather requirements, also knows a good amount of data engineering.
205 00:25:05.670 ⇒ 00:25:21.669 Uttam Kumaran: you see Awash is also… so everybody sort of, like, does have a core thing where they’re really awesome at, right? And so I know that for you, you’re really awesome at the financial analysis work. Where you’re gonna become, like, you just have to start to bridge is go into the areas that you’re uncomfortable.
206 00:25:21.740 ⇒ 00:25:32.650 Uttam Kumaran: And learn more about the data modeling side. I’m telling you, you’ll… it’s like, you’ll just unlock a lot of ways that you’ll start to be able to fight for yourself to get these things going, versus waiting on.
207 00:25:32.650 ⇒ 00:25:33.470 Sezim Zhenishbekova: Yeah.
208 00:25:33.470 ⇒ 00:25:47.539 Uttam Kumaran: You know, and your ability to understand more of, like, what is possible will grow once you start understanding why data modeling is powerful. Similarly on the BI side, like, I can’t recommend enough to learn Tableau and to learn these BI tools, because…
209 00:25:47.540 ⇒ 00:25:47.900 Sezim Zhenishbekova: Yeah.
210 00:25:47.900 ⇒ 00:25:51.290 Uttam Kumaran: It’s gonna allow you to get beyond just being a finance analyst.
211 00:25:51.440 ⇒ 00:25:56.049 Uttam Kumaran: You know, how am I truly just, like, I could come in and analyze any type of data.
212 00:25:56.170 ⇒ 00:26:07.809 Uttam Kumaran: Still, your expertise is gonna be on finance, but bringing that to other industries and other types of work is what is going to be the superpower, right?
213 00:26:07.900 ⇒ 00:26:17.169 Uttam Kumaran: So, that’s what I would… I would kind of urge you to do. I guess, like, what’s been… what’s, like, what do you think now, being in here? Like, where do you want to grow more?
214 00:26:17.270 ⇒ 00:26:24.520 Uttam Kumaran: Like, do you think, like, going more technical is your interest? Are you, like, where… what has been, like, energizing in the things that you’ve done?
215 00:26:24.940 ⇒ 00:26:26.079 Uttam Kumaran: This past month.
216 00:26:27.270 ⇒ 00:26:40.490 Sezim Zhenishbekova: I think everything that I really was doing was energizing. It’s very hard to tell, just, like, after a month, like, what I loved, what I didn’t like, because it was really, everything was pretty new. I was, like, jumping into the problem, solving it.
217 00:26:40.640 ⇒ 00:26:57.260 Sezim Zhenishbekova: So I would love to spend more time on that, but I love… I enjoy technical stuff as much as I like to manage people, but in order to manage people, what to do, how to do, I need to have a good senior level of understanding so that, people can trust what I’m saying and execute on it.
218 00:26:57.260 ⇒ 00:27:01.180 Sezim Zhenishbekova: And I don’t have that type of confidence in me as of now.
219 00:27:01.180 ⇒ 00:27:20.559 Sezim Zhenishbekova: I think with time, when I start, like, thinking more on the executive level, once I, like, brush up my analytics, I will be more, straightforward with that. But, like, gathering business requirements, talking to clients, that’s a common thing that I used to do as a product manager in the past, so I know pretty much how to do that.
220 00:27:21.170 ⇒ 00:27:23.990 Sezim Zhenishbekova: Yeah, drilling down into our requests.
221 00:27:24.360 ⇒ 00:27:27.990 Uttam Kumaran: Yeah, and another thing, you know, I sent this to you on your first week is.
222 00:27:28.150 ⇒ 00:27:30.950 Uttam Kumaran: just lean on communicating. I’m so happy.
223 00:27:30.950 ⇒ 00:27:31.700 Sezim Zhenishbekova: Yeah.
224 00:27:31.700 ⇒ 00:27:36.169 Uttam Kumaran: Like, I think a lot of people, especially, I’m sure, from your… from your industry and finance.
225 00:27:36.370 ⇒ 00:27:50.910 Uttam Kumaran: filled with a lot of, like, assholes who are just like, don’t email me, or, like, don’t be very short. This is not the situation. You’re gonna get in trouble here if you don’t communicate, you know? So I’m always gonna push you to over-communicate, over-communicate, like.
226 00:27:50.910 ⇒ 00:27:59.440 Uttam Kumaran: don’t worry about feeling dumb, don’t worry about… nobody here thinks in that way. We don’t have people like that in our company, you know?
227 00:27:59.600 ⇒ 00:28:08.520 Uttam Kumaran: So, my only urge is for you to really, like, set your ego aside and try to ask questions. It will help you learn.
228 00:28:08.520 ⇒ 00:28:09.260 Sezim Zhenishbekova: Yeah.
229 00:28:09.260 ⇒ 00:28:28.090 Uttam Kumaran: fast, you know, and everybody, as you can tell, is willing to help, but if you… if you spin your wheels, all it’s gonna lead you to do is get frustrated, and then you’re not gonna hit your deliverable. And so, every step of the way, in any channel, just ask the question. That’s always something that, especially… there’s certain folks who I know
230 00:28:28.200 ⇒ 00:28:36.749 Uttam Kumaran: have a tendency to sort of, like, be more reserved. And I see, because I’m in every channel, so I read every… basically every single message.
231 00:28:37.180 ⇒ 00:28:43.770 Uttam Kumaran: I know who’s not talking, and not talking in this company is a sign of, like, you’re not, something’s wrong.
232 00:28:43.970 ⇒ 00:29:01.280 Uttam Kumaran: You know? And so I’m always watching for who is not communicating more, because I know you’re still… you’re working on something that you could use some help on, right? And so what you’ll see is our best people, Awash, Amber, Sam, Casey, Mustafa, they communicate a lot.
233 00:29:01.280 ⇒ 00:29:10.919 Uttam Kumaran: Right? And they’re not afraid, because they… their objective is to execute the right thing as fast as possible, and so communication is the only way. Otherwise, you gotta wait for the meeting.
234 00:29:11.310 ⇒ 00:29:25.839 Uttam Kumaran: Where you don’t have the meeting, you wait for the next stand-up, and I was like, damn, I wish I had this… whatever you just said, 60 seconds, I wish I knew yesterday. Those are the things you just want to eliminate, right? And nobody here is ever going to fault you for slacking too much. Like, I slack too much, you know? So…
235 00:29:26.360 ⇒ 00:29:32.070 Uttam Kumaran: that’s probably one thing. And then my last question here is, like, what can we… how can we help you speed up your learning?
236 00:29:32.300 ⇒ 00:29:33.819 Uttam Kumaran: Like, what do you need from us?
237 00:29:34.160 ⇒ 00:29:39.150 Uttam Kumaran: To help grow faster and learn more, like, what could we do better?
238 00:29:40.240 ⇒ 00:29:58.189 Sezim Zhenishbekova: So definitely, I need more hours, I think, like, than, like, what I did. I, I was actually, like, also on the site, reading a lot about, health tech, like, hymns and hers, and all those industries, just to get just an understanding of the business in general, and the environment itself.
239 00:29:58.190 ⇒ 00:30:02.880 Sezim Zhenishbekova: So that would be very helpful. Other than that,
240 00:30:03.570 ⇒ 00:30:05.939 Sezim Zhenishbekova: Yeah, I think it’s just that I feel like…
241 00:30:05.940 ⇒ 00:30:06.620 Uttam Kumaran: Okay.
242 00:30:06.620 ⇒ 00:30:14.600 Sezim Zhenishbekova: I, like, I mean, it’s months already passed, so I think it’s… it’s already… I’m already on board, so now it’s time to get up to speed.
243 00:30:15.110 ⇒ 00:30:15.780 Uttam Kumaran: Okay.
244 00:30:15.780 ⇒ 00:30:16.200 Sezim Zhenishbekova: Yeah.
245 00:30:16.200 ⇒ 00:30:16.830 Uttam Kumaran: Perfect, cool.
246 00:30:17.400 ⇒ 00:30:22.260 Uttam Kumaran: Robert, do you have a good sense of, like, scope for SEZM for next month?
247 00:30:23.580 ⇒ 00:30:43.270 Robert Tseng: Yeah, I think we should, you know, since we’re doing all these reviews with people, we need to decide, like, who we’re gonna put on which client, and then, like… I mean, Seism’s capacity, like, what you’re… are you able to go up to full-time, or kind of, like, what do you… what do you want, like, what do you want from us? Like, I guess you’re kind of, like, around 10… 10 to 20, I forgot exactly.
248 00:30:43.270 ⇒ 00:30:48.879 Sezim Zhenishbekova: Yeah, for now, it’s from 10 to 20, but I can go up till, like, 30.
249 00:30:48.880 ⇒ 00:31:01.330 Robert Tseng: 30? Okay, so we could possibly ramp her up to 30, we just have to see if we want to do that. I don’t… yeah, I’ve already captured time at Eden, so, like, I think we have that moving forward. I forgot what number I put out there, but we can decide if.
250 00:31:01.330 ⇒ 00:31:01.860 Sezim Zhenishbekova: Here’s another one.
251 00:31:01.860 ⇒ 00:31:03.329 Robert Tseng: One we can bring her onto.
252 00:31:03.720 ⇒ 00:31:04.190 Uttam Kumaran: Thanks.
253 00:31:04.190 ⇒ 00:31:04.910 Sezim Zhenishbekova: Yeah.
254 00:31:05.440 ⇒ 00:31:16.760 Sezim Zhenishbekova: Yeah. So you basically, like, devote, like, 10 hours, right? Like, I had 10 hours for Eden, so you work like that, that, like, you have specific hours for specific projects, and then it can be…
255 00:31:17.320 ⇒ 00:31:17.820 Uttam Kumaran: Not, like.
256 00:31:17.820 ⇒ 00:31:18.410 Sezim Zhenishbekova: Like, not…
257 00:31:18.410 ⇒ 00:31:19.490 Uttam Kumaran: I think.
258 00:31:19.490 ⇒ 00:31:21.869 Sezim Zhenishbekova: Or, like, more hours going. Yeah.
259 00:31:21.870 ⇒ 00:31:29.940 Uttam Kumaran: For folks that are working, like, a full 40 hours, they’re basically sometimes working on anywhere from typically 2 to 4 clients.
260 00:31:29.990 ⇒ 00:31:32.140 Sezim Zhenishbekova: And they manage their time.
261 00:31:32.140 ⇒ 00:31:42.510 Uttam Kumaran: So we’re not as explicit. I mean, we will get a little bit more mindful in terms of margin, but most of what we’re hoping for is just take on the work that you can mail and the time that.
262 00:31:43.100 ⇒ 00:31:50.119 Uttam Kumaran: nail it, and don’t take on more. And so, in this situation, as we ramp people up, we do give guidance on hours.
263 00:31:50.560 ⇒ 00:32:02.720 Uttam Kumaran: That’s mainly because, like, we also understand how long it takes to do the work, and so we don’t want to give you too little time. But also, we don’t have… sometimes for, like, insomnia, we don’t have, like, tons of budgets, right?
264 00:32:02.720 ⇒ 00:32:03.310 Sezim Zhenishbekova: Yeah.
265 00:32:03.310 ⇒ 00:32:03.660 Uttam Kumaran: So…
266 00:32:03.660 ⇒ 00:32:04.780 Sezim Zhenishbekova: Yeah, that makes sense.
267 00:32:04.780 ⇒ 00:32:15.480 Uttam Kumaran: it’s sort of… we have to flip some upper limits. But we do… we do have at least, like, one or two other clients where we need this type of work, and there’s work that will kind of stretch you in other ways, and so…
268 00:32:15.480 ⇒ 00:32:18.120 Sezim Zhenishbekova: I’m open to considering that.
269 00:32:18.140 ⇒ 00:32:19.800 Uttam Kumaran: Yeah.
270 00:32:21.260 ⇒ 00:32:22.040 Sezim Zhenishbekova: Yeah.
271 00:32:22.830 ⇒ 00:32:24.130 Robert Tseng: Yeah. Yeah, and…
272 00:32:26.000 ⇒ 00:32:33.709 Robert Tseng: I was just gonna say, okay, so we’ll decide on, like, what a, like, maybe we bump you up to, yeah, a certain number of hours, and then…
273 00:32:33.710 ⇒ 00:32:53.220 Robert Tseng: yeah, I guess that’ll impact, kind of, what you spend your time on. I’m sure it won’t all go to Ian, like, it’ll go… we’ll have to find… we have some internal stuff that we would want to do, maybe on our own finances, our own financial modeling, but also, like, yeah, I think, like Utom said, on another… we would want to at least put you on another client.
274 00:32:53.380 ⇒ 00:32:55.580 Robert Tseng: Give you a different one. Yeah. Yeah.
275 00:32:55.580 ⇒ 00:32:58.580 Sezim Zhenishbekova: Yeah, that would be nice. Yeah.
276 00:32:59.080 ⇒ 00:33:04.580 Sezim Zhenishbekova: I think that’s good. Thank you. Thank you so much for such a thorough review, it’s very helpful.
277 00:33:05.150 ⇒ 00:33:05.820 Robert Tseng: Yeah, of course.
278 00:33:05.820 ⇒ 00:33:07.410 Sezim Zhenishbekova: I’m glad to. Yeah.
279 00:33:07.950 ⇒ 00:33:12.630 Robert Tseng: you can, you can always reach out to us, you have a, you have a, you know, channeled with us, so, yeah.
280 00:33:12.630 ⇒ 00:33:17.780 Uttam Kumaran: And send me work, send me work to review. Like, I’m not getting any financial analysis to look at, I’m bummed.
281 00:33:18.350 ⇒ 00:33:22.909 Uttam Kumaran: I gotta, like, find it in the corners of Slack somewhere and read it. Like, I would love to take a.
282 00:33:22.910 ⇒ 00:33:28.709 Sezim Zhenishbekova: I haven’t done any financial forecasting yet, but as soon as it’s done, I’ll pin you.
283 00:33:28.910 ⇒ 00:33:34.069 Uttam Kumaran: Yeah, it’s been a while. We don’t do any, like, serious Excel work, so I would, like… I love that stuff.
284 00:33:34.070 ⇒ 00:33:39.670 Sezim Zhenishbekova: So, but, like, I like your comment. I think we should have some common templates, like, systems.
285 00:33:39.670 ⇒ 00:33:40.170 Uttam Kumaran: I agree.
286 00:33:40.170 ⇒ 00:33:43.330 Sezim Zhenishbekova: It’s for worksheets and Excels, so, like, we’re just.
287 00:33:43.330 ⇒ 00:33:43.790 Uttam Kumaran: In Google.
288 00:33:43.790 ⇒ 00:33:47.179 Sezim Zhenishbekova: And then just use it for everything, with logos and everything?
289 00:33:47.180 ⇒ 00:33:47.760 Uttam Kumaran: Yeah, yeah.
290 00:33:47.760 ⇒ 00:33:48.770 Sezim Zhenishbekova: in October.
291 00:33:48.770 ⇒ 00:34:02.149 Uttam Kumaran: Dana can help with all that, yeah, they can get you all the coloring, and we should have standard workbook templates, and then eventually, like, we’re gonna do very common… like, for example, we’re gonna do forecasting and things like that very often, so we’ll just have templates for these things.
292 00:34:02.150 ⇒ 00:34:02.510 Sezim Zhenishbekova: Yeah.
293 00:34:02.510 ⇒ 00:34:11.209 Uttam Kumaran: But again, you’re the first person who has a real, like, background in Excel analysis, so… yeah, I’m like, I’m happy to help, you know, push any of that forward.
294 00:34:11.219 ⇒ 00:34:14.809 Sezim Zhenishbekova: Yeah. But again, what’s your vision for the upcoming… Yeah, of course, go ahead.
295 00:34:14.810 ⇒ 00:34:15.910 Uttam Kumaran: Yeah, go, go, go, go ahead.
296 00:34:16.340 ⇒ 00:34:21.580 Sezim Zhenishbekova: I was wondering what’s your vision for this upcoming year? Like, what kind of clients do you want to bring on?
297 00:34:21.580 ⇒ 00:34:22.260 Uttam Kumaran: So just…
298 00:34:22.260 ⇒ 00:34:23.149 Sezim Zhenishbekova: I hear your vision.
299 00:34:23.610 ⇒ 00:34:25.170 Sezim Zhenishbekova: of the company.
300 00:34:25.860 ⇒ 00:34:33.810 Uttam Kumaran: We want to work with larger, more complicated clients.
301 00:34:34.010 ⇒ 00:34:35.680 Uttam Kumaran: Make way more money.
302 00:34:36.010 ⇒ 00:34:38.479 Uttam Kumaran: And continue to have the best team.
303 00:34:39.010 ⇒ 00:34:41.710 Uttam Kumaran: on planet Earth, doing this type of work.
304 00:34:42.010 ⇒ 00:34:42.749 Sezim Zhenishbekova: I have a…
305 00:34:42.949 ⇒ 00:34:46.019 Uttam Kumaran: That’s my… that’s my vision, you know?
306 00:34:46.019 ⇒ 00:34:50.709 Sezim Zhenishbekova: But notice I didn’t say we want to hire 100 people. I’m not interested in, like.
307 00:34:50.709 ⇒ 00:35:02.909 Uttam Kumaran: hiring hundreds of people, interested in having the best team, whatever size that is, and I also want to continue to work for bigger, tougher clients, because that’s where the money is, you know?
308 00:35:03.029 ⇒ 00:35:08.540 Sezim Zhenishbekova: And complicated by… complicated, what do you mean by complicated, right?
309 00:35:08.540 ⇒ 00:35:11.349 Uttam Kumaran: You’re solving problems that not many other people can solve.
310 00:35:11.580 ⇒ 00:35:12.180 Sezim Zhenishbekova: Hmm.
311 00:35:12.180 ⇒ 00:35:16.960 Uttam Kumaran: At least… and that could be solving in a timeframe that not many other people can do.
312 00:35:17.140 ⇒ 00:35:23.000 Uttam Kumaran: The types of problems, The complexity, you know, but… like.
313 00:35:23.550 ⇒ 00:35:28.609 Uttam Kumaran: Yeah, that’s more of my interest, because then it just lowers the amount of people that we’re competing with.
314 00:35:29.350 ⇒ 00:35:29.860 Sezim Zhenishbekova: people can.
315 00:35:29.860 ⇒ 00:35:38.580 Uttam Kumaran: come in and just do simple things, you know? And so we’re continuing to build a business that can do a lot of things that are all fairly complicated, and you have a one-stop shop
316 00:35:38.910 ⇒ 00:35:39.860 Uttam Kumaran: Kind of do that.
317 00:35:40.200 ⇒ 00:35:46.000 Uttam Kumaran: And then, my last, probably, goal is that everybody uses AI, like, 100 times more. So…
318 00:35:46.110 ⇒ 00:35:57.129 Uttam Kumaran: So today, we didn’t talk… I didn’t press anything on, like, you using AI and things like that, but that’ll become more of a story, like, next, like, 6 months. Every single role will have a…
319 00:35:57.350 ⇒ 00:36:06.019 Uttam Kumaran: clear directive on leveraging AI to speed up the work you do, improve the outputs, give yourself feedback, and stuff like that, so…
320 00:36:06.990 ⇒ 00:36:08.590 Sezim Zhenishbekova: Yeah, I like that.
321 00:36:08.590 ⇒ 00:36:10.590 Uttam Kumaran: Just, like, casual… casual goals.
322 00:36:12.540 ⇒ 00:36:17.229 Uttam Kumaran: Did the number one in the world. That’s it, there’s no number two.
323 00:36:17.230 ⇒ 00:36:17.740 Sezim Zhenishbekova: So…
324 00:36:17.740 ⇒ 00:36:20.420 Uttam Kumaran: Never heard… you’re never gonna hear about number two.
325 00:36:21.790 ⇒ 00:36:26.419 Uttam Kumaran: If we strive to be the number one, then we’ll end up somewhere close, and that’ll be just fine.
326 00:36:27.060 ⇒ 00:36:32.139 Sezim Zhenishbekova: Yeah. That’s awesome. Yeah, I’m setting that still cursor.
327 00:36:32.140 ⇒ 00:36:53.590 Sezim Zhenishbekova: Henry showed me how he runs, like, some queries directly from Tableau, like, he can just request it, and, like, it finds all the information, so it’s amazing. Yeah. But for some reason, I’m having issues logging in. Yesterday, I was on a call with Demilada, we still couldn’t figure it out, so he issued me access with my different email, and there I will have a pro account, so…
328 00:36:53.610 ⇒ 00:36:57.290 Sezim Zhenishbekova: Once I have that set up, I will use more AI.
329 00:36:57.870 ⇒ 00:37:05.720 Uttam Kumaran: No, no, no, we’ll do… we’re gonna do a logger cursor, like, walkthrough for everybody, maybe next week or in January, so don’t worry, yeah, we’ll get you all set up.
330 00:37:06.900 ⇒ 00:37:24.750 Sezim Zhenishbekova: Yeah, and then, other than that, I understand that you also, like, think, like, I’m, like, big in finance and stuff, but at the same time, I just got out of the school, I worked for a bit, but I wasn’t, like, exposed to it for more than 3 years into it, so I would love some help from the company
331 00:37:24.750 ⇒ 00:37:28.930 Sezim Zhenishbekova: helping me to boost my, like, forecasting skills, like, what’s more helpful?
332 00:37:28.930 ⇒ 00:37:31.470 Sezim Zhenishbekova: So I think also… I will do that. Yeah.
333 00:37:31.470 ⇒ 00:37:46.240 Uttam Kumaran: Yeah, building a relationship with Greg is also gonna be good. He’s gonna be joining us a little bit in a bigger capacity, too. He’s also an ex-professor. Greg, Stoutenberg, you may have not worked with him yet, but he’s on our analyst team.
334 00:37:46.530 ⇒ 00:37:53.149 Uttam Kumaran: I think, like, we need some type of, like, brain trust on, like, just leveling up. I mean, I need to read, like, everything he says, but…
335 00:37:53.360 ⇒ 00:38:11.639 Uttam Kumaran: I think there’s a lot we can learn from him, and, like, he’s thought very… and him and Robert, I think, are probably the most senior on the analysis side, so I do want to think about, like, if we were to put, like, a curriculum together, like, what we could all learn. But he has some really, really good… so I’ll introduce you guys in Slack. He’s off.
336 00:38:11.740 ⇒ 00:38:22.730 Uttam Kumaran: But, you should speak with him and say hi, and he’ll have some good resources from you, and he’ll also learn a lot. But the other thing is, like, just share your work publicly, and ask for feedback, like.
337 00:38:22.950 ⇒ 00:38:31.710 Uttam Kumaran: again, me, at least you’ll get feedback from me in a way, guaranteed. And then there are other people will… other people will… I’ll start to tag other people if they don’t get feedback.
338 00:38:31.710 ⇒ 00:38:32.880 Sezim Zhenishbekova: Sounds good. Sounds good.
339 00:38:32.880 ⇒ 00:38:50.689 Uttam Kumaran: You know more than anyone is that that’s the only way you can learn, right? So unless you hear what you’re doing well or not well, and the more times you can get the feedback, the faster you’re gonna learn. So if you only get feedback once a week, then it’s only, like, 30, 40 weeks of feedback. If you start asking for feedback every day, like.
340 00:38:50.830 ⇒ 00:38:55.699 Uttam Kumaran: I’m telling you, you’ll accelerate. So think about that and lean on that a little bit more.
341 00:38:55.700 ⇒ 00:38:57.939 Sezim Zhenishbekova: Sounds good. Yeah.
342 00:38:58.800 ⇒ 00:38:59.430 Uttam Kumaran: Okay.
343 00:38:59.430 ⇒ 00:39:02.600 Sezim Zhenishbekova: Yeah, there’s no more questions from my side. Thank you.
344 00:39:02.860 ⇒ 00:39:05.370 Uttam Kumaran: Thank you so much. Enjoy the break, safe travel.
345 00:39:05.370 ⇒ 00:39:14.980 Sezim Zhenishbekova: Yes, enjoy your break, and have a happy holidays. Thank you. See you next year, I guess. Okay, bye.
346 00:39:15.140 ⇒ 00:39:16.000 Robert Tseng: Alright.