Meeting Title: Uttam_James Date: 2025-03-07 Meeting participants: Uttam Kumaran, James Freire
WEBVTT
1 00:00:53.830 ⇒ 00:00:54.970 Uttam Kumaran: Hey!
2 00:00:55.120 ⇒ 00:00:56.240 James Freire: Hey? How you doing.
3 00:00:56.240 ⇒ 00:00:57.409 Uttam Kumaran: Good! How are you?
4 00:00:57.410 ⇒ 00:00:58.670 James Freire: Doing. Well, thanks.
5 00:00:58.670 ⇒ 00:01:04.690 Uttam Kumaran: Thank you so much sorry for the rescheduling and the confusion. I know it’s Friday afternoon, so appreciate it.
6 00:01:04.690 ⇒ 00:01:10.590 James Freire: Yeah, it’s no problem. This is pretty. Sometimes it’s easier because I I usually don’t have other meetings scheduled.
7 00:01:10.590 ⇒ 00:01:24.879 Uttam Kumaran: Okay, cool. Thank you for taking the time. Yeah, I’d love to sort of share where we’re at. Probably I’ll I’ll give you a little sense of the company as well, and then you know, I’m not sure how much Jodi is shared, but like happy to walk through anything on our side.
8 00:01:25.040 ⇒ 00:01:27.849 James Freire: Yeah, yeah, sure. Yeah. Tell me what? Tell me what you got going on.
9 00:01:27.850 ⇒ 00:01:55.620 Uttam Kumaran: Cool. Yeah, so yeah. Jodi’s a good friend of mine we connected like last year, and I’ve been sort of chatting, so I’m glad he put us in touch. My name is you, Tom. I run Brainforge. Brainforge is a data analytics and AI consultancy. Well, we’ve been in business since 2023. My background is in data, engineering, leading data teams, sort of done full time and contract work at the Enterprise level. And then went on to sort of start this business.
10 00:01:56.290 ⇒ 00:01:58.690 Uttam Kumaran: A few. Yeah, roughly, about
11 00:01:59.080 ⇒ 00:02:01.428 Uttam Kumaran: a year and a half ago.
12 00:02:01.990 ⇒ 00:02:08.390 Uttam Kumaran: So yeah, I think the the biggest thing that we’re really stuck in right now is just trying to get in
13 00:02:08.996 ⇒ 00:02:10.009 Uttam Kumaran: senior talent.
14 00:02:10.504 ⇒ 00:02:15.525 Uttam Kumaran: I think we we have a couple of junior folks on the analysis side that
15 00:02:16.020 ⇒ 00:02:21.969 Uttam Kumaran: are struggling with some of a tableau request from a particular client. Then they need sort of like.
16 00:02:23.100 ⇒ 00:02:40.099 Uttam Kumaran: I don’t know. I’ve done tableau reporting for about 2 years. You know, I’ve done a lot of work in Looker and a few other bi tools. Really for this client. They they’re like, they’re like executive level they just want, like really crisp executive views of their business.
17 00:02:40.672 ⇒ 00:02:55.620 Uttam Kumaran: And we have some people on our on our analyst side. That are probably more geared to just like going and doing point analysis instead of like setting up the dashboards. And so we’re sort of looking for someone short term to come in and like fill a little bit of that gap of that gap.
18 00:02:56.056 ⇒ 00:03:03.230 Uttam Kumaran: To give you a little bit of sense of the client. The client is Eden health. They are a glp, one
19 00:03:03.923 ⇒ 00:03:14.330 Uttam Kumaran: e-commerce. They sell a variety of glp. One solutions through a variety of mediums like gummies, different sort of things.
20 00:03:14.540 ⇒ 00:03:40.459 Uttam Kumaran: They’re doing a lot in revenue, but are really lacking in their insight into their business, and but have a lot of hunger towards making daily and weekly improvements to sort of scale the business up. We came in a brought about 3. We came about like, 2 months ago, we came in. They had a data analyst sort of like a half operations person and a data engineer that we’re sort of working
21 00:03:40.660 ⇒ 00:03:54.919 Uttam Kumaran: like a little bit all over the place. They set up bigquery. They had like 50 tables, all sort of materialized queries, scheduled queries. They they were using looker studio. But we’re just sort of unhappy with the quality of their data.
22 00:03:55.306 ⇒ 00:04:13.020 Uttam Kumaran: Their ability to get insights quickly. And of course, like really, really not happy with the visual presentation. So we came in. We actually absorbed that analyst. And we’re sort of running point on a lot of their core initiatives. We have some core roadblocks around.
23 00:04:13.476 ⇒ 00:04:28.099 Uttam Kumaran: Profited profitability, dashboards, marketing dashboards, and like an executive kpi overview. There are definitely we pushed out a 1st version of but it could definitely use some love
24 00:04:28.339 ⇒ 00:04:48.919 Uttam Kumaran: but also look, one of the things they they want is, they want us to sort of own the story of like what is a dashboard telling them and what insights they can get out of it? And I think that’s where, of course, as you know, like, there’s a there’s a challenge there for folks that are more junior is that it’s 1 thing to just get the line chart out. But then, if you get put on the hot seat of like, okay.
25 00:04:49.100 ⇒ 00:04:59.170 Uttam Kumaran: like, what do I do? You know it’s a. It’s a different story. So that’s sort of where we’re at. But I guess, like, let me know any questions or anything I can answer. Happy to.
26 00:04:59.840 ⇒ 00:05:04.505 James Freire: No, no, I mean, it’s it’s just interesting problem. So it’s like, it’s 1 of those they
27 00:05:05.280 ⇒ 00:05:15.291 James Freire: they they do. They have an idea of the kpis. They they even want to look at, or they’re just like we got all this stuff. How do we do improvement in what we’re doing? And
28 00:05:15.570 ⇒ 00:05:18.820 Uttam Kumaran: They are. I would say they’re
29 00:05:19.370 ⇒ 00:05:23.199 Uttam Kumaran: they’re heavily opinionated, but like not confident.
30 00:05:24.300 ⇒ 00:05:24.860 James Freire: What
31 00:05:25.980 ⇒ 00:05:26.789 Uttam Kumaran: You see what I mean?
32 00:05:26.790 ⇒ 00:05:29.289 James Freire: Yeah, I know. What are they heavily opinionated about?
33 00:05:29.861 ⇒ 00:05:47.319 Uttam Kumaran: Like they’ll have heavy opinions about. We should do things this way. But it’s funny they’ll always be like, well, what does the data say, but it’s kind of like a double edged sword, like they they think about ways to model their business. What Kpis? But they do really want to use data to to do that.
34 00:05:47.530 ⇒ 00:06:06.749 Uttam Kumaran: But for our analysts, of course, it’s a tough environment, because you sort of have to have a really good idea of like what the alternatives are. But they are looking to us for that. We’ve we’ve worked with a lot of e-commerce, and the reason why we are able to go into this business and sort of say, here’s how you should measure your business, because a lot of the work we’ve done in the past. But there is some nuances to this
35 00:06:07.180 ⇒ 00:06:11.930 Uttam Kumaran: client and the type, the the way they’re model. We’re modeling their product and
36 00:06:12.050 ⇒ 00:06:15.430 Uttam Kumaran: order data. They have subscriptions. They have bundles
37 00:06:15.800 ⇒ 00:06:29.230 Uttam Kumaran: the but like it’s for a prescription like it’s for a prescription process. So you you buy like a glp one treatment. But there’s like 4 different subsequent orders that happen.
38 00:06:29.570 ⇒ 00:06:30.460 James Freire: Yeah.
39 00:06:30.460 ⇒ 00:06:35.740 Uttam Kumaran: But you pay upfront for it, but you can get a refund in the middle of it.
40 00:06:35.860 ⇒ 00:06:40.710 Uttam Kumaran: and so they have transactions. They have orders. They have treatments.
41 00:06:40.990 ⇒ 00:06:47.710 Uttam Kumaran: They have a bunch of product like it’s like a, it’s like A, it’s like a mixed bag of dimensionality that even
42 00:06:48.610 ⇒ 00:06:51.370 Uttam Kumaran: they didn’t even really have a clear understanding that.
43 00:06:52.170 ⇒ 00:06:54.909 Uttam Kumaran: Oh, there’s like a bunch of nuance to
44 00:06:55.050 ⇒ 00:07:08.810 Uttam Kumaran: how we’re modeling this data. And then, of course, the bigger challenges they’ve they’ve realized revenue issues because they can only realize the money when the the month in which the service is provided, even though it’s paid upfront.
45 00:07:08.910 ⇒ 00:07:12.319 Uttam Kumaran: So they have transaction revenue, and they’ve realized revenue.
46 00:07:12.470 ⇒ 00:07:18.520 Uttam Kumaran: But beyond the modeling, I think they’re really just like it’s an executive leadership team that just wants to have
47 00:07:18.620 ⇒ 00:07:21.319 Uttam Kumaran: really great insights into their business.
48 00:07:22.292 ⇒ 00:07:25.990 Uttam Kumaran: Starting with the core financials and the core.
49 00:07:26.788 ⇒ 00:07:29.230 Uttam Kumaran: The core products around profitability.
50 00:07:30.720 ⇒ 00:07:31.430 James Freire: Yeah.
51 00:07:33.110 ⇒ 00:07:43.370 Uttam Kumaran: And so we’ve our team. We’ve modeled a lot of the data. And we’re continuing doing that. We’re it’s still sitting on bigquery. We just moved them to tableau over the past few weeks.
52 00:07:43.841 ⇒ 00:07:53.980 Uttam Kumaran: But we’re struggling because we don’t have another Senior tableau. I mean, I’m I’m no longer doing a lot of client work right now. So we need a we need sort of a senior tableau person.
53 00:07:54.710 ⇒ 00:07:59.490 James Freire: Yeah, the tableau, the tableau is not the hard part. It’s like the.
54 00:07:59.490 ⇒ 00:07:59.900 Uttam Kumaran: Yes.
55 00:08:00.010 ⇒ 00:08:02.230 James Freire: It’s the financials. It’s the.
56 00:08:02.800 ⇒ 00:08:20.239 James Freire: It’s the financial analytics of the business. Yes, it’s like, there’s a it’s not a traditional funnel where you’re getting like you’re looking at like a conversion rate of what they’re throwing out there online. It’s like, okay, how these different ads working for us. It’s like you have that medical aspect of
57 00:08:20.830 ⇒ 00:08:23.729 James Freire: like you said there’s different
58 00:08:24.480 ⇒ 00:08:32.010 James Freire: different sub outcomes depending on how it goes. And then you have the not getting. The transaction is not realized until.
59 00:08:32.010 ⇒ 00:08:49.799 Uttam Kumaran: The concept of bundles like. So you can bundle products together and buy them can buy individual products. So they they want to look at profitability at many different levels, like they want to look at individual product profitability. They want to look at bundles. But what is profitability? This is also where they want to see ad spend
60 00:08:50.150 ⇒ 00:09:17.059 Uttam Kumaran: based profitability, meaning what is a profitability of a product category after we factor in not only ad spend, but shipping, discounts, refunds, and cost of goods sold. The problem is, how do you? They were not. They were not marking their campaigns as attributed to one specific product variant. They were saying, this product group, we’re going to run ads towards. And then we’re like, Okay, how do we attribute? How do we attribute the cost to the variant
61 00:09:17.360 ⇒ 00:09:18.380 Uttam Kumaran: Associate. They’re like what.
62 00:09:18.380 ⇒ 00:09:20.559 James Freire: It was just one big, but it was just like in one.
63 00:09:20.560 ⇒ 00:09:25.829 Uttam Kumaran: They said they, we said, they said, spread it out evenly by by revenue.
64 00:09:26.430 ⇒ 00:09:37.160 Uttam Kumaran: And so, but it’s also like this is where we we said, we can do that. But you guys just need to name your campaigns better. So we can do better matching of the spend to the product.
65 00:09:37.640 ⇒ 00:09:40.790 Uttam Kumaran: And that’s something we caught for them. And they’re they’re sort of going.
66 00:09:43.970 ⇒ 00:09:57.740 Uttam Kumaran: you know. But yeah, there is. This sort of there is a there is a nuance. There is some nuances to the transaction. There’s just other stuff where we’re dealing with bad data in. And so we have to tell them we have to either work with their vendors to say we need better stuff.
67 00:09:57.850 ⇒ 00:10:06.210 Uttam Kumaran: or we need to work with our marketing team to be like, you need to name campaigns better. We need to better Utm terms so that we can do the attribution better.
68 00:10:07.740 ⇒ 00:10:09.509 Uttam Kumaran: And sort of handling that, too.
69 00:10:11.550 ⇒ 00:10:16.120 James Freire: And like, what are they? So like in terms of vendors? What are they using just out of curiosity?
70 00:10:16.400 ⇒ 00:10:22.370 Uttam Kumaran: Yeah, I mean, they’re they’re on they’re on all the major ad platforms. They’re using shippo for shipments
71 00:10:22.470 ⇒ 00:10:29.177 Uttam Kumaran: they’re using. This is the real. The real trouble is, they’re using this company called bask, which is
72 00:10:29.950 ⇒ 00:10:42.159 Uttam Kumaran: like a shopify, for like medical e-commerce, it’s like a startup. And the data is really really bad. They’re moving to shopify, but we are stuck with that
73 00:10:42.760 ⇒ 00:10:44.030 Uttam Kumaran: in the short term
74 00:10:45.860 ⇒ 00:10:57.709 Uttam Kumaran: and that’s that leads to a lot of issues. And then they use they’re using north beam for ad spend. They’re using Zendesk for customer service.
75 00:10:59.130 ⇒ 00:11:07.910 Uttam Kumaran: but the usual stuff I feel like we’re for example, we are building a customer service dashboard for them that we have the Zendesk data modeled
76 00:11:08.080 ⇒ 00:11:18.139 Uttam Kumaran: and that dashboard we did a mock up for, and we’re building out. It’s really this like executive leadership and product profitability that continues to be
77 00:11:18.300 ⇒ 00:11:23.620 Uttam Kumaran: a real pain, you know, for them and and for us to sort of like
78 00:11:24.030 ⇒ 00:11:28.169 Uttam Kumaran: start to tell them this, the story of like what’s actually happening in their business.
79 00:11:29.510 ⇒ 00:11:33.879 James Freire: Yeah, yeah, it just seems more complicated than just the tub. It’s like
80 00:11:34.030 ⇒ 00:11:36.590 James Freire: to me. The tableau sounds like the easy part.
81 00:11:36.820 ⇒ 00:11:38.510 James Freire: It’s the we did.
82 00:11:38.960 ⇒ 00:11:43.510 Uttam Kumaran: I mean, yeah, but it’s like it’s not like, it’s just you. We have a team for sure.
83 00:11:43.510 ⇒ 00:11:44.230 James Freire: Oh, okay.
84 00:11:44.690 ⇒ 00:11:48.139 Uttam Kumaran: Yeah, so so we have. So we have 2 other analysts.
85 00:11:48.758 ⇒ 00:11:55.129 Uttam Kumaran: and an analytics engineer. And basically a product owner and a project manager. So
86 00:11:55.390 ⇒ 00:11:58.360 Uttam Kumaran: yeah, there’s 5 people, basically.
87 00:11:58.360 ⇒ 00:12:05.659 James Freire: So what’s the shortcoming? So you turn like? So far we’ve been when you’ve been working with the tableau stuff like, what is the problem that you’re running into.
88 00:12:05.930 ⇒ 00:12:17.789 Uttam Kumaran: We have. We have 2 people developing tableau, one of the people on the team. She’s only working part time. And the other guy on the team is just slow. And he, this is the 1st time using tableau. So he’s not good. Yeah.
89 00:12:18.620 ⇒ 00:12:25.164 James Freire: Hmm! And it’s just there’s nothing special with the tablet. It’s just regular tableau charts, nothing.
90 00:12:25.980 ⇒ 00:12:43.410 Uttam Kumaran: I mean, I I yeah, if, like, I I think you, you know what I’m talking about. Yeah, it’s like usual stuff. It’s not anything like advanced Geo, like crazy shit. We’re we’re actually mostly in charge of the design, too. So like they just want something that they’ll be like, Damn, this is really good. And I see what I need to see
91 00:12:43.680 ⇒ 00:12:45.760 Uttam Kumaran: our real problem.
92 00:12:45.910 ⇒ 00:12:54.609 Uttam Kumaran: There is nuances to the modeling of the data, and we’ve solved a lot of those. It’s just that like we can’t get the tableau dashboards and iterations
93 00:12:54.830 ⇒ 00:12:56.310 Uttam Kumaran: fast enough out.
94 00:12:57.323 ⇒ 00:13:02.329 Uttam Kumaran: And like, yeah, I just don’t have another analyst right now to sort of put on this client.
95 00:13:02.330 ⇒ 00:13:13.129 James Freire: Yeah, and what would the ideal and like, what would the ideal analyst look like for you in terms of like, you know, commitment of time, and and you know the cycle in terms of developing that sort of thing.
96 00:13:13.270 ⇒ 00:13:20.440 Uttam Kumaran: Yeah, I think our cycles will get longer right now. It’s probably like we’re we’re basically like on like week long cycles.
97 00:13:22.130 ⇒ 00:13:34.009 Uttam Kumaran: I mean one. I just want someone who can who like knocking out tableau dashboards is like a breeze. That’s really what we’re looking for. Ideally. I would also like to have someone who in that process
98 00:13:34.160 ⇒ 00:13:43.579 Uttam Kumaran: learns a couple of nuggets that we can start to share with the business ideally. We want to, of course, be more on that side where we are, and we’re not just like
99 00:13:43.830 ⇒ 00:13:53.939 Uttam Kumaran: spinning out dashboards and tossing it over the fence where we’re finding the ways for them to improve their business. That’s what that’s what that’s what we do. But I just don’t have a
100 00:13:54.420 ⇒ 00:13:57.979 Uttam Kumaran: I don’t have another tableau expertise person. It would just take me.
101 00:13:59.080 ⇒ 00:14:05.239 Uttam Kumaran: It would take me a bit longer to go find someone full time for that internally. So we’re sort of looking for a patch right now.
102 00:14:06.940 ⇒ 00:14:08.278 James Freire: Yeah, I’m trying to think of like,
103 00:14:08.470 ⇒ 00:14:13.460 Uttam Kumaran: Oh, it seems like it’s probably anywhere from like 10 to 20 HA week if I had to guess.
104 00:14:13.730 ⇒ 00:14:18.510 Uttam Kumaran: Okay, it may fluctuate, but I don’t know. Like, what do you think about that?
105 00:14:18.510 ⇒ 00:14:19.724 James Freire: Yeah, I mean
106 00:14:20.706 ⇒ 00:14:25.922 James Freire: I mean, I’ve done, you know, through throughout, you know home depot now
107 00:14:26.770 ⇒ 00:14:34.999 James Freire: and Amazon. But I had them as a customer, and then when I worked there, I did a lot of tableau stuff for them. I don’t do a ton of like the past year. I’ve not done a lot of tableau stuff.
108 00:14:35.000 ⇒ 00:14:35.430 Uttam Kumaran: Okay.
109 00:14:35.430 ⇒ 00:14:38.099 James Freire: Done visualizations. My whole career with doing data, science.
110 00:14:38.100 ⇒ 00:14:41.679 Uttam Kumaran: Yeah, that’s that’s more of like. Again, I did tableau
111 00:14:41.840 ⇒ 00:14:45.390 Uttam Kumaran: for 2 years, like 4 years ago. It’s the same thing. It’s like.
112 00:14:45.390 ⇒ 00:14:46.130 James Freire: Yeah, that’s right.
113 00:14:46.130 ⇒ 00:15:03.260 Uttam Kumaran: That’s what I’m like, yeah, I guess to take the edge off. I’m not worried about that. It’s like we’re actually like, the problem is very easy, like the guy we have doing. Tableau cannot do tableau, so I’m trying to move him off. But we we still need like he’s moving, probably at a 10th of the speed of like that, you would move.
114 00:15:03.710 ⇒ 00:15:07.549 Uttam Kumaran: And so we just need someone to come in and just move it
115 00:15:08.330 ⇒ 00:15:10.510 Uttam Kumaran: even like a regular pace, you know.
116 00:15:10.510 ⇒ 00:15:14.691 James Freire: Yeah. Yeah. Yeah. If I mean, I could definitely do that.
117 00:15:15.626 ⇒ 00:15:18.883 James Freire: you know, 10 to 10 to 20 HI could do
118 00:15:19.962 ⇒ 00:15:24.970 James Freire: it’s easier for me if I don’t have to spend a lot of time gathering requirements.
119 00:15:24.970 ⇒ 00:15:25.400 Uttam Kumaran: Okay.
120 00:15:25.400 ⇒ 00:15:30.380 James Freire: Like if you have like. Even if you were to give me like, Hey, we it does have to be. It could be kind of broad.
121 00:15:30.380 ⇒ 00:15:36.000 Uttam Kumaran: Give me a sense. Give me a sense of bad requirements, and give me a sense of of really great requirements, and I’ll tell you where we are.
122 00:15:36.000 ⇒ 00:15:42.671 James Freire: It’s like, right here, bad requirements. When you said like, the the client is like, Yeah, we have a strong opinion, but I don’t know what I want.
123 00:15:42.910 ⇒ 00:15:54.710 Uttam Kumaran: So so yeah, you won’t be. So don’t worry like we’re not gonna throw you in front of the client. We’re gonna there’s a there’s 1 there is, there is a filter between. There. Second is, we have. We have pretty good idea of what we want, and we.
124 00:15:54.710 ⇒ 00:15:55.260 James Freire: Okay.
125 00:15:55.260 ⇒ 00:15:57.839 Uttam Kumaran: I’m giving you the overview of like, yeah.
126 00:15:57.840 ⇒ 00:16:02.380 James Freire: I mean, I’m very. I’m very I I’m good at abstract. I’m very creative. I’m good.
127 00:16:02.380 ⇒ 00:16:03.380 Uttam Kumaran: Okay. Okay. Yeah.
128 00:16:03.380 ⇒ 00:16:04.470 James Freire: I’m very visual person.
129 00:16:04.470 ⇒ 00:16:14.259 Uttam Kumaran: Give you the the 4 1 1 and the whole deal. But yeah, you’ll you’ll have like to give you a sense we’ll have. We have mock ups for most of our stuff.
130 00:16:14.420 ⇒ 00:16:17.970 Uttam Kumaran: and that’s like what we actually go and agree upon with the
131 00:16:17.970 ⇒ 00:16:20.950 Uttam Kumaran: that I usually work with, which is more, and I’m even used to.
132 00:16:20.950 ⇒ 00:16:21.670 James Freire: Yeah, yeah, yeah.
133 00:16:21.670 ⇒ 00:16:26.629 Uttam Kumaran: That’s probably like the extent of like the best. There are situations where it’s like.
134 00:16:26.820 ⇒ 00:16:31.670 Uttam Kumaran: hey, can we just get like something simple around revenue, where we can show breakdowns on.
135 00:16:31.670 ⇒ 00:16:32.949 James Freire: Yeah, that’s not a problem.
136 00:16:32.950 ⇒ 00:16:35.020 Uttam Kumaran: Yeah, but it’s definitely not like.
137 00:16:35.280 ⇒ 00:16:39.382 Uttam Kumaran: go into a meeting and whip it up like live. So it’s I think we’re aligned there. It’s
138 00:16:39.610 ⇒ 00:16:42.930 James Freire: Yeah, I’m sorry, I think. Like you tell. Tell me some of the the stuff I’m like
139 00:16:43.040 ⇒ 00:16:46.300 James Freire: I studied for the Cfi never took. I never took it because I couldn’t.
140 00:16:46.300 ⇒ 00:16:47.750 Uttam Kumaran: No, no, no! And in fact, like.
141 00:16:47.750 ⇒ 00:16:48.510 James Freire: It’s.
142 00:16:48.510 ⇒ 00:16:52.110 Uttam Kumaran: Complex. But I we’ve solved a lot of those problems.
143 00:16:52.110 ⇒ 00:16:52.440 James Freire: Yeah.
144 00:16:52.440 ⇒ 00:16:55.080 Uttam Kumaran: Which is great like, I feel good about
145 00:16:55.410 ⇒ 00:17:04.220 Uttam Kumaran: the way. And again, it’s you have a team so like a question about hey? How is this defined? We have an analytics engineer that will will explain to you where in the warehouse to get it, and how it’s defined.
146 00:17:04.220 ⇒ 00:17:11.120 James Freire: Okay, yeah. And I work a lot with bigquery, anyway. So all my, I mean all my research, I’ve always done. I always
147 00:17:11.650 ⇒ 00:17:20.039 James Freire: conceptualize things visually. Like I used to use. I don’t do use it so much now at home depot, but I’ve always used R and Gg. Plot for, like
148 00:17:20.410 ⇒ 00:17:21.790 James Freire: all my stuff.
149 00:17:21.790 ⇒ 00:17:22.560 Uttam Kumaran: Oh, nice!
150 00:17:22.560 ⇒ 00:17:24.769 James Freire: Yeah, oh, I love it, cause it.
151 00:17:24.770 ⇒ 00:17:26.469 Uttam Kumaran: Get into like database stuff.
152 00:17:26.968 ⇒ 00:17:49.860 James Freire: You know I got into. I started at a hedge fund in my late twenties. I’d workedcom when I was a teenager, and then I got an internship at a hedge fund, and I worked my way up to the trading desk, and I then I started trading at Portfolio just because I was really good at, because I knew Unix and all those things very well. So I was able to automate a lot of things a lot of traders just didn’t know how to do, because they’re just financial people
153 00:17:49.870 ⇒ 00:18:14.909 James Freire: in R is one of those early tools in that you could work with data frames kind of like the way you have pandas. Now, that was like the only thing, and I was crashing excel, trying to do this stuff I was trying to do in Bb script, and I got R. Because one of the Phds on my teams showed it to me. I was that there’s open up the whole quant world to me with with statistics was using R, and I just went down that rabbit hole. I always use it for data. Mini violation.
154 00:18:14.910 ⇒ 00:18:15.640 Uttam Kumaran: Nice.
155 00:18:15.640 ⇒ 00:18:27.049 James Freire: You know, using tidyverse and all that stuff and ggplot is nice, because it’s the only thing that I could truly put what I had in my mind like onto into the screen. And that’s that’s how I got into it.
156 00:18:27.300 ⇒ 00:18:30.239 Uttam Kumaran: What sort of Davis stuff are you doing now? So you’re at home depot now.
157 00:18:31.040 ⇒ 00:18:32.752 James Freire: Yeah. So I’m doing
158 00:18:33.500 ⇒ 00:18:40.320 James Freire: I’m doing basically their data engineering for Jira reporting essentially because they have everything standardized.
159 00:18:40.570 ⇒ 00:19:07.029 James Freire: They have everything standardized across the whole organization for project management to a point where, like they even have, everybody does the same cadence and sprints when they start and end, and they have a compliance thing where people have to put out certain amount of like tasks in their sprints, backlog for X amount of quarters out and everything. So they were doing everything on spreadsheets. So I’m kind of taking all their stuff and centralizing it into data warehousing.
160 00:19:07.290 ⇒ 00:19:13.630 Uttam Kumaran: Okay. Yeah, alright. Well, that’s far. That’s far away from what? What tableau stuff we’re talking about.
161 00:19:13.630 ⇒ 00:19:15.380 James Freire: Yeah, it’s like, it’s all kind of.
162 00:19:15.380 ⇒ 00:19:15.980 Uttam Kumaran: Yeah, yeah, yeah.
163 00:19:16.990 ⇒ 00:19:24.260 James Freire: Not originally because of tableau. That’s that’s the part. And I saw that everything else was a mess. I was like, you know, I can data engineer all this stuff for you, too.
164 00:19:24.260 ⇒ 00:19:24.920 Uttam Kumaran: Yeah.
165 00:19:24.920 ⇒ 00:19:27.790 James Freire: So we can get it. You know the right way. So.
166 00:19:28.190 ⇒ 00:19:41.480 Uttam Kumaran: Yeah, I mean, that’s how I got into this, too. So like I worked at, I worked at we work. That was my 1st job at a college, and I was on their bi team. And then that company scaled from like 6,000 to 15,000 people. The Bi team scaled from like 6 of us
167 00:19:41.910 ⇒ 00:19:43.970 Uttam Kumaran: to like 40 50 people.
168 00:19:44.765 ⇒ 00:20:00.139 Uttam Kumaran: And then I went to this another startup that was smaller than that called flow code. I was the 1st data engineer hire there, so hired everybody built that team and then was a full stack. Pm, on our data product. So we were selling Apis and analytics like customer facing dashboards.
169 00:20:00.531 ⇒ 00:20:14.379 Uttam Kumaran: And then I led product at another data startup. And then I started this company. And but this has been great because we it’s just like every company we work with is struggling so heavily with the same set of problems. We do a lot of work in E-com, and and B, 2 b Saas.
170 00:20:14.380 ⇒ 00:20:15.199 James Freire: Yeah, yeah.
171 00:20:15.200 ⇒ 00:20:40.599 Uttam Kumaran: A lot of it is like we come in. If they don’t have a warehouse, we, we implement Snowflake. We centralize all their data. We model it all in Dbt, and then we try to get to the point where they have dashboards. The real thing we’re trying to get towards now is not just doing dashboards. It’s actually like trying to help them be like, Hey, we are probably the apart from the CEO. We probably have the best understanding of your business, because we’ve gone through every single.
172 00:20:40.600 ⇒ 00:20:44.009 James Freire: Yeah, that’s what happens when you have like when you when.
173 00:20:44.010 ⇒ 00:20:45.319 Uttam Kumaran: Yeah, you know, yeah.
174 00:20:45.320 ⇒ 00:20:46.979 James Freire: You know the business really? Well.
175 00:20:46.980 ⇒ 00:21:10.240 Uttam Kumaran: But a bad business will not take advantage of those data people. They’ll be like, just build a dashboard. The reason why people like us is because we will go one step further and be like we found something. You should go do this like. We found that these customers have way higher. Ltv, and like this, Geo, we need to. You need to get this, your marketing team and put dollars behind that. And like, that’s why people like us because we’re.
176 00:21:10.600 ⇒ 00:21:24.730 Uttam Kumaran: I think sometimes you have both people. You’ll get people who are strategy only, and then they’re like, well, we don’t touch the keyboard. Then you’ll get technical people who are like, Yeah, I wrote everything. And I’m like, Do you understand? Like, where, who’s going to use this? The business like, No, I’m like.
177 00:21:25.130 ⇒ 00:21:47.400 Uttam Kumaran: do you understand what products they’re selling, or what type of business they are? No, I’m like, so what do you like? Well, how do you? What, and so I was always pushing the boundary on like I was always going above, and being like well, I know the let me put me with the product owner like. I’ll tell them what they what’s going on with their product. And then, now, that’s sort of why we’re in business is we do a lot of that, almost like
178 00:21:47.580 ⇒ 00:21:51.450 Uttam Kumaran: the full stack. But we’re trying to get into the mode where we’re giving insights.
179 00:21:51.670 ⇒ 00:22:15.840 Uttam Kumaran: and we have a lot of clients that they make a lot of money. But there’s they’re just sitting on so much that they haven’t seen about their business that’s in the data. And so we’re in this phase where for a lot of our clients, we’re doing dashboards. But we want to move towards insights fast. But you know, it’s like it’s tough to find data. People that think like that like, there’s a lot of analysts who are just like, I’ll do the dashboard. And I’m like, Okay, but
180 00:22:16.200 ⇒ 00:22:28.779 Uttam Kumaran: if the number has like, if it says 60 million versus 6 million, would you have noticed? Like, right? So they’re like, no, it’s summed up properly, and like I got the table, and I’m like, no, but that’s not like that’s not.
181 00:22:28.780 ⇒ 00:22:29.949 James Freire: Most of, yeah, I mean.
182 00:22:29.950 ⇒ 00:22:50.040 Uttam Kumaran: Good enough, and you know, if you’re in the if you’re in a bank, you’re done, it’s over like if you put some, if you put like garbage like that in front of people. And so we’re at the point where we’re trying to basically find talent like that. We’re just kind of growing pain. So I’m sort of patching where where I can with with people who can sort of solve that. But.
183 00:22:50.040 ⇒ 00:23:01.319 James Freire: Yeah, yeah, it’s like, that’s, you know, that’s I was. I guess I was lucky because I started from the finance. Well, I had the it background, the financial side. Looking all that data. I mean, I I got a lot of insights from that in.
184 00:23:01.320 ⇒ 00:23:01.980 Uttam Kumaran: Yes.
185 00:23:01.980 ⇒ 00:23:07.359 James Freire: And because I was like at the hedge fund I was at, I had. I was one of the few people that had access to everybody’s books.
186 00:23:07.360 ⇒ 00:23:07.880 Uttam Kumaran: Yeah.
187 00:23:07.880 ⇒ 00:23:12.290 James Freire: I was able to see. I was like the guy’s name that I worked for. His name is Steve Cohen. If you look him up on wicked.
188 00:23:12.290 ⇒ 00:23:13.370 Uttam Kumaran: Yeah, yeah, of course.
189 00:23:13.370 ⇒ 00:23:15.929 James Freire: You know, worked at Sach capital. So.
190 00:23:15.930 ⇒ 00:23:17.210 Uttam Kumaran: No way. Wow!
191 00:23:17.210 ⇒ 00:23:20.279 James Freire: Oh, yeah, yeah, I try. Sat on the training floor like, like 20 feet.
192 00:23:20.280 ⇒ 00:23:21.970 Uttam Kumaran: No way.
193 00:23:21.970 ⇒ 00:23:26.020 James Freire: Yeah, yeah, you’re like, what? Yeah. Some people don’t know what that is, or some people know what it is. It’s kind of funny.
194 00:23:26.020 ⇒ 00:23:33.500 Uttam Kumaran: Oh, my God! What do you mean? How do you not know who that says, yeah, this is the biggest story. It’s the big, it’s the best performing guy still, like.
195 00:23:33.500 ⇒ 00:23:34.300 James Freire: Yeah, yeah, yeah, he’s.
196 00:23:34.300 ⇒ 00:23:34.950 Uttam Kumaran: Yeah.
197 00:23:34.950 ⇒ 00:23:37.180 James Freire: He’s a genius, I mean, like.
198 00:23:37.180 ⇒ 00:23:40.609 Uttam Kumaran: Well, yeah. So tell me, what was it like? Tell me, what was it like?
199 00:23:40.610 ⇒ 00:23:43.499 James Freire: Oh, so what was it like?
200 00:23:43.500 ⇒ 00:23:45.660 Uttam Kumaran: I don’t know. That’s a broad question. But like
201 00:23:46.040 ⇒ 00:23:50.439 Uttam Kumaran: dude, that’s that’s not like a small story. That’s incredible. I had no idea.
202 00:23:50.440 ⇒ 00:24:00.367 James Freire: So A really good family friend of ours. He had been working for Steve for a long time, and I went back to school in my twenties.
203 00:24:00.830 ⇒ 00:24:15.396 James Freire: I was thinking about doing, you know, law or history something like that. But that was just like I stay. I stay with my it career. Then I saw how how well he was doing, and my dad was into technical analysis on the like as a hobby.
204 00:24:15.970 ⇒ 00:24:28.790 James Freire: So I asked for. I asked him, could you hook me up with an internship? So I got an internship, because I you know I knew unix very well, and I got to know the people that ran the trading desk, the execution desk, and from there
205 00:24:28.790 ⇒ 00:24:46.457 James Freire: I just asked the guy that ran his name is Phil Vilhauer? I asked him, for I was like, Hey, can I get? Can I get my next internship next summer and winter with you? And he’s like, yeah. And he even got me. They’re able to get me a workstation at the end of the trading desk. Sit on there. And I basically spent like a winter and a summer like
206 00:24:46.750 ⇒ 00:24:56.689 James Freire: using Bloomberg, yeah, and learning how to, you know, connect with the Apis and all that type of stuff. And I got a job. They actually offered me a job as a trading clerk.
207 00:24:58.190 ⇒ 00:25:12.369 James Freire: on one of the funds that they called the Select Fund, which was kind of a portfolio construction based off of what all the portfolio managers were trading, and we had certain liquidity rules and all sorts of things. And we’d take it was kind of like an etf of all the.
208 00:25:12.784 ⇒ 00:25:13.199 Uttam Kumaran: Okay.
209 00:25:13.200 ⇒ 00:25:14.899 James Freire: Foods there, all the ideas.
210 00:25:14.900 ⇒ 00:25:15.710 Uttam Kumaran: Interesting, yeah.
211 00:25:15.710 ⇒ 00:25:19.149 James Freire: So then, after a while, I mean.
212 00:25:19.340 ⇒ 00:25:26.040 James Freire: I did a lot. I learned it pretty well. I was able to automate a lot of stuff and do this and that, and they ended up letting me trade and run it for a while.
213 00:25:26.601 ⇒ 00:25:33.130 James Freire: But yeah, I set up at the train desk I sat. I started working in August 2,008, when the market started to melt down.
214 00:25:33.330 ⇒ 00:25:34.040 Uttam Kumaran: Yeah.
215 00:25:34.040 ⇒ 00:25:43.259 James Freire: And they had actually gave me a job right before that happened. And I was just like what the I mean when, like the market was tanking like that big day when they, the tarp, didn’t pass.
216 00:25:43.499 ⇒ 00:25:46.130 Uttam Kumaran: Monday or black Tuesday. What? I forgot what it is. Yeah.
217 00:25:46.130 ⇒ 00:25:47.589 James Freire: I think it was on a Tuesday. Yeah.
218 00:25:47.590 ⇒ 00:25:49.370 Uttam Kumaran: Black Tuesday, or something like that. Yeah.
219 00:25:49.370 ⇒ 00:25:58.529 James Freire: Yeah, the market, like, Super crashed. I was working on the trading desk. We’re just like watching the screen, just like I mean, the Bluebird terminal is just like it was like zooming out, you know.
220 00:25:58.530 ⇒ 00:26:00.040 Uttam Kumaran: Yeah, it’s insane.
221 00:26:00.440 ⇒ 00:26:03.213 James Freire: But yeah, no. I sat. I mean,
222 00:26:03.970 ⇒ 00:26:08.749 James Freire: I didn’t talk to Steve a lot. I emailed a lot with them, talk to him sometimes.
223 00:26:09.207 ⇒ 00:26:18.530 James Freire: Just because he was directly involved in that portfolio, because it was basically an aggregation of everything that was going on there. So I mean, I had to email him every I did like. Send him P. And L. Reports and.
224 00:26:18.530 ⇒ 00:26:19.220 Uttam Kumaran: And it’s so.
225 00:26:19.220 ⇒ 00:26:20.190 James Freire: Like twice a day.
226 00:26:20.190 ⇒ 00:26:21.080 Uttam Kumaran: That’s awesome.
227 00:26:21.080 ⇒ 00:26:24.320 James Freire: Yeah. And I’m a guy with a history degree. So I’m like, what the hell am I doing?
228 00:26:24.585 ⇒ 00:26:25.700 James Freire: Okay, what the hell of.
229 00:26:25.700 ⇒ 00:26:33.370 Uttam Kumaran: No, but that’s what I heard they hired. That’s what they it’s, you know. It’s about making money. It’s even for our business. I I don’t think I ask anyone what they studied.
230 00:26:33.500 ⇒ 00:26:35.799 Uttam Kumaran: I’m just like, can you do the job?
231 00:26:35.800 ⇒ 00:26:39.420 James Freire: Yeah, yeah, one of the programmers didn’t even have a Ged. I mean, he was.
232 00:26:39.420 ⇒ 00:27:00.119 Uttam Kumaran: You know, data people come from everywhere, too, like I happen to be engineering. But like, I work with great people who are like philosophy majors, whatever. You kind of stumble into this because it’s like a mix of the business and technical. And it’s not something you really can learn like without being on the job. Most of the people they’re either from like the marketing side or the the business side they they like.
233 00:27:00.400 ⇒ 00:27:15.749 Uttam Kumaran: They just had to figure out, excel one day, and then they’re like went down the rabbit hole, or they come from the other side, where they’re technical, and then they’re like, well, let me go solve this data problem. Someone is like, Hey, can you do? We don’t have a data person. And they did it. That’s no one ever is like, maybe nowadays people are like studying to be
234 00:27:15.900 ⇒ 00:27:27.910 Uttam Kumaran: data engineers or data analysts. But I learned every single thing on the job. I mean, I I learned, taught myself sequel like. So I got I I went to Bucknell, which is in Central PA. I
235 00:27:28.000 ⇒ 00:27:52.165 Uttam Kumaran: I got a I cold emailed my way to wework through an alumni, and then I 2 days they put me. I bombed the sequel interview. The guy liked me. He was like, I like you, but like you’ve bombed this like the next one, I’m going to tell him to grill. You should learn sequel, and I got an interview in person. They were in Chelsea like near the bed bath beyond, on like 6th and 6th and 18th and
236 00:27:52.810 ⇒ 00:28:01.652 Uttam Kumaran: I went 2 days before in a holiday inn and 34th Street and taught myself sequel in the business center, and then went to the interview.
237 00:28:01.980 ⇒ 00:28:08.490 Uttam Kumaran: Nice, I know I’m from. I’m from Connecticut, from Stanford. So okay, alright, yeah.
238 00:28:08.490 ⇒ 00:28:11.110 James Freire: Yeah. So I know that is, that’s wild. Yeah.
239 00:28:11.110 ⇒ 00:28:11.660 Uttam Kumaran: Yeah.
240 00:28:11.820 ⇒ 00:28:29.789 James Freire: It’s like, especially work like, you know, the financial industry, especially at the time. There was not. There was no books really on, except as the academic side, though, like what I worked in specifically, I did a lot of work in what was called implementation shortfall strategies. So reducing slippage and trading execution.
241 00:28:29.790 ⇒ 00:28:30.150 Uttam Kumaran: Okay.
242 00:28:30.357 ⇒ 00:28:34.510 James Freire: That’s not a thing. You could just go. Even at that point you could not just Google it. It was.
243 00:28:34.510 ⇒ 00:28:35.390 Uttam Kumaran: Yeah, yeah.
244 00:28:35.390 ⇒ 00:28:40.250 James Freire: Is such obscure niche thing. And then I learned about tack files, ticker and quote files.
245 00:28:40.250 ⇒ 00:28:40.730 Uttam Kumaran: Yeah.
246 00:28:40.730 ⇒ 00:28:51.670 James Freire: No, you couldn’t get stuff like that. We had to like, you know. You had to roll your own. I had to roll my own python scripts to like. Ingest this data it would take overnight to go through like a month’s worth attack files.
247 00:28:51.670 ⇒ 00:28:52.350 Uttam Kumaran: Yeah.
248 00:28:52.590 ⇒ 00:28:53.010 James Freire: Yeah.
249 00:28:53.010 ⇒ 00:29:01.529 Uttam Kumaran: My, you know, I I studied finance in school, and like I come from a financial background, and the big project I worked on is, I worked on the wework. s, 1.
250 00:29:02.010 ⇒ 00:29:06.760 Uttam Kumaran: So I worked with a lot of the bankers on the wework side of things, putting together the data.
251 00:29:06.960 ⇒ 00:29:33.669 Uttam Kumaran: And it was crazy because the day the s. 1 came out. Of course the Ipo didn’t happen, but I like printed out the s. 1 i was like I produced the chart like that’s mine, but I also like I don’t know. I’m really glad I didn’t, because my life would have been different, but I wanted to go into banking, didn’t get it, which was good, because, like life would have been insane, and I probably I would have been way unhappier like being in the startup world in New York
252 00:29:34.160 ⇒ 00:29:38.129 Uttam Kumaran: was different, different level of stress. But yeah, yeah, it’s been.
253 00:29:38.130 ⇒ 00:29:48.019 James Freire: Well, it’s you know. I mean, like large places like the thing that drives me nuts about some place. I didn’t home Amazon. Not quite, but like Home Depot, it takes
254 00:29:48.210 ⇒ 00:29:50.169 James Freire: very long time to get things done. Well.
255 00:29:50.170 ⇒ 00:29:53.899 Uttam Kumaran: Yeah. So tell me why you’re at home depot, if you’re coming from that background.
256 00:29:54.480 ⇒ 00:29:57.759 Uttam Kumaran: And you’re like moving faster from Connecticut, like, how are you at.
257 00:29:57.760 ⇒ 00:29:59.350 James Freire: I’m from Virginia. I live in Virginia now.
258 00:29:59.350 ⇒ 00:29:59.830 Uttam Kumaran: Okay. Okay.
259 00:30:00.213 ⇒ 00:30:06.350 James Freire: It is, you know, after sac. Then you had the Dodd-frank bill that came out.
260 00:30:06.350 ⇒ 00:30:06.670 Uttam Kumaran: Yeah.
261 00:30:06.670 ⇒ 00:30:23.750 James Freire: Nobody wanted to do prop trading, I mean, I talked with a lot of people I knew like a citibank, and all over the place, and nobody was hiring prop traders. So I came down to Northern Virginia. My brother’s suggestion, because it’s a big tech hub. But I got working at Amazon. So
262 00:30:23.790 ⇒ 00:30:41.659 James Freire: and I worked in the data center space. And that’s kind of I was like, that’s before, like the data science role was really a thing. It’s like, well, that’s basically what I was doing. It wasn’t called that. But you know, doing the quantitative trading was very, it was computer programming and statistics and data. So I started leveraging that at Amazon. That’s what I was doing.
263 00:30:41.830 ⇒ 00:30:45.189 James Freire: I just kind of stuck with that for a long time. Yeah.
264 00:30:45.720 ⇒ 00:30:46.310 Uttam Kumaran: Cool.
265 00:30:46.790 ⇒ 00:30:55.820 Uttam Kumaran: Well, yeah, I don’t. I don’t know. Like, how does the how does the opportunity sound like? How can I? I would love to, if you’re interested, would love to introduce you, maybe to one more person that’s closer to the client. But.
266 00:30:55.820 ⇒ 00:31:11.529 James Freire: Yeah, yeah, I mean, like the pace I mean, I’m looking to expand a little bit more. My, you know my my clientele base. I guess you’d call it so. You know I have. I do have my own Llc, so if you want to work that way, that’s totally fine, and
267 00:31:11.990 ⇒ 00:31:15.630 James Freire: it’s easy for me. It’s probably for you, too, so.
268 00:31:16.110 ⇒ 00:31:25.790 Uttam Kumaran: Yeah. And then, like, what do you think about like the client? The work, like, I think we’ll probably have to get you some more insight into that. But maybe I would love as the next step, maybe to connect you. One more person.
269 00:31:25.790 ⇒ 00:31:28.289 James Freire: Yeah, yeah, fine. See? You like, learn the yeah.
270 00:31:28.290 ⇒ 00:31:31.460 Uttam Kumaran: They can give you the they can give you the actual like skating on what’s
271 00:31:31.610 ⇒ 00:31:47.320 Uttam Kumaran: what it is I just want. I gave you the high level about like our interaction with the client. I actually think a lot of our issues with them now have not have been earlier. There were, because we we had a lot of modeling issues. Now, it’s our our speed at which we can get them. Insights is where we’re jammed
272 00:31:47.778 ⇒ 00:31:54.741 Uttam Kumaran: but also again, like, if it works with them. We have several other clients that we’re we’re doing this for
273 00:31:55.230 ⇒ 00:32:04.369 Uttam Kumaran: And like, I think, having someone with your background again, we’re we have some junior analysts. We’re trying to hire people that have an eye for like
274 00:32:04.540 ⇒ 00:32:18.020 Uttam Kumaran: not just building great dashboards was sort of like explaining, Okay, what is it was, actually, these people are trying to get out of that, because the one thing I told the team today is I was like client will ask you for a dashboard, but they actually want the answer, you know. And so
275 00:32:18.350 ⇒ 00:32:28.420 Uttam Kumaran: the dashboard is a way forward. Fine! But like we’re here to give them the insight. You know. And so people with that sort of mindset is like.
276 00:32:28.420 ⇒ 00:32:29.010 James Freire: Yeah.
277 00:32:29.010 ⇒ 00:32:29.590 Uttam Kumaran: It’s a farm.
278 00:32:29.590 ⇒ 00:32:42.840 James Freire: I mean really good. I mean, my my talent is kinda that’s why I really got into the trading very, very well. Things that like you talked about the 6 or 60 million like weird, weird numbers when I, especially when I know the math behind it.
279 00:32:42.840 ⇒ 00:32:43.370 Uttam Kumaran: Yes.
280 00:32:43.370 ⇒ 00:32:48.904 James Freire: Very, very well. If you go on my Linkedin page you’ll probably see my trading setup.
281 00:32:49.250 ⇒ 00:32:51.880 Uttam Kumaran: Oh, wait. Yeah, I feel like, I have, yeah, I did. I did. Yeah, yeah.
282 00:32:51.880 ⇒ 00:33:17.089 James Freire: Yeah, yeah. So those are all like all the green and red. Those are all the position monitors. So it’s like a list of all the positions, and you know, like the beta, and you know the P. And L. And all that stuff is on there. But like I do the trading world so well, even though it was not like a thing. If a number is like, you know, has an extra 0. But like I just know something was off, but I was able to like, you know, go and drill down like. Why, trading that equity, for whatever reason.
283 00:33:17.090 ⇒ 00:33:17.520 Uttam Kumaran: Yeah.
284 00:33:17.834 ⇒ 00:33:31.369 James Freire: So having an eye for like, why, things look weird, or or you know the numbers behind something. It’s not just getting the charts right? It’s digging down to see what’s the explanation. The numbers, you know behind it that that matter.
285 00:33:31.370 ⇒ 00:33:48.120 Uttam Kumaran: And I also think our setup is good because we we sort of. We come in and like typically people hire, they’ll hire a tableau person. But then it’s a modeling problem. Or it’s a data engineering problem, right? Like, that’s the thing about us is we’re getting clients where we’re like we’re taking on the full scope. And so
286 00:33:48.560 ⇒ 00:34:10.379 Uttam Kumaran: we solve those things in order, because I know that if we were to get to hire to just do the dashboard. We can’t happen unless you fix the warehouse and make sure stuff is available. We have staging and and prod environments, and it can’t work until we have the data on time. So we need a great Etl tool. So we come in and we do the whole thing, you know. So we basically do like full stack data team as a service.
287 00:34:10.380 ⇒ 00:34:30.869 James Freire: Yeah. And I could do. I mean the whole the whole gamut, you know, you know, even installing Linux, you know. I mean from from back in the day. So I mean all those, all those things go into my tool chest. So digging into. Why, something’s not right. I’ve always had to get my own data out of things to do my research. And there’s always that joke. It’s like 80% of doing data. Science is like doing your donor
288 00:34:31.199 ⇒ 00:34:32.699 James Freire: data engineering, anyway.
289 00:34:32.699 ⇒ 00:34:34.053 Uttam Kumaran: Yeah. Yeah.
290 00:34:34.989 ⇒ 00:34:47.059 Uttam Kumaran: Okay. Great. Alright. So how about I put you in touch with one more person, and then we can probably go from there. I think it would love to just hear how that conversation goes. And then, yeah, I feel like this. I think this is right up your alley.
291 00:34:47.060 ⇒ 00:34:48.870 James Freire: Yeah, absolutely. It sounds cool.
292 00:34:48.870 ⇒ 00:34:52.249 Uttam Kumaran: Okay. Alright. Well, really, great meeting you. Thank you so much for the time.
293 00:34:52.250 ⇒ 00:34:53.709 James Freire: Yeah, yeah, it was good chatting with you.
294 00:34:53.710 ⇒ 00:34:56.710 Uttam Kumaran: Yeah, yeah, the sac story is awesome. Like, I.
295 00:34:56.719 ⇒ 00:34:58.319 James Freire: So next time we talk, I can tell you.
296 00:34:58.320 ⇒ 00:35:01.966 Uttam Kumaran: Next time we talk I I try to read because I feel like I watched.
297 00:35:02.400 ⇒ 00:35:14.240 Uttam Kumaran: Well, I watched the documentary about. Well, I watch billions, of course, and so that gives me a lot of I feel like, but I watch a bunch of recent Stephen Cohen. Well, cause just I mean over the
298 00:35:14.820 ⇒ 00:35:15.470 Uttam Kumaran: years.
299 00:35:15.470 ⇒ 00:35:16.640 James Freire: Gabe Plotkin.
300 00:35:16.640 ⇒ 00:35:17.400 Uttam Kumaran: Yes.
301 00:35:17.400 ⇒ 00:35:18.810 James Freire: Yeah, I used to work them.
302 00:35:19.130 ⇒ 00:35:22.140 Uttam Kumaran: Away, yes, no way.
303 00:35:22.140 ⇒ 00:35:26.259 James Freire: Yeah, yeah, I used to. I used to go through his books. I used to. I used to work with him. Yeah.
304 00:35:26.710 ⇒ 00:35:29.563 Uttam Kumaran: Wow! Well, oh, cause I read I read
305 00:35:30.515 ⇒ 00:35:32.344 Uttam Kumaran: what’s the book it’s called?
306 00:35:33.970 ⇒ 00:35:36.901 Uttam Kumaran: the it’s like the black edge, or
307 00:35:37.600 ⇒ 00:35:39.640 Uttam Kumaran: hold on, let me tell you, it’s a
308 00:35:42.460 ⇒ 00:35:51.540 Uttam Kumaran: yeah. This was yeah, it’s called Black Edge. It’s it’s about. Yes, Black Edge was about.
309 00:35:51.790 ⇒ 00:35:52.939 Uttam Kumaran: Si see.
310 00:35:53.470 ⇒ 00:35:55.460 James Freire: Really, how did I never hear this black.
311 00:35:55.460 ⇒ 00:35:57.689 Uttam Kumaran: You should read it. Oh, my God, it’s like
312 00:35:57.860 ⇒ 00:36:01.290 Uttam Kumaran: I mean, I feel like you said like I don’t think I can nerd out
313 00:36:01.470 ⇒ 00:36:06.830 Uttam Kumaran: to anybody but someone like you. But it’s like a it’s a not. It’s a really.
314 00:36:07.080 ⇒ 00:36:09.390 Uttam Kumaran: I mean you were there. So it’ll be like.
315 00:36:09.390 ⇒ 00:36:10.629 James Freire: Yeah, I could. Also.
316 00:36:10.630 ⇒ 00:36:17.050 Uttam Kumaran: Recognize a lot of the people. But yes, it’s basically about the inside of trading story from from sac and all. And all of that.
317 00:36:17.050 ⇒ 00:36:24.470 James Freire: Yeah. Well, I’d say, like the insider trading stuff is, kinda there’s some people like that. We’re working under him.
318 00:36:24.840 ⇒ 00:36:39.700 James Freire: you know. Yeah, like Steve, insider trading makes it’s like the dollar amount that he was accused of a long time ago. The amount he was. He was trading a billion dollar book. Right? Yeah. The guy was so brilliant I would have a position
319 00:36:40.170 ⇒ 00:36:45.910 James Freire: spreadsheet with the positions he would trade. I would have sectored out. He hedged in his brain.
320 00:36:46.130 ⇒ 00:37:00.840 James Freire: his sector positions, and all all his positions. He knew how to hedge them, using S. And P. Minis, and he didn’t use a spreadsheet. I had a spreadsheet to see what his like his his, you know Beta was. He could do it in his head for all those 5 sectors.
321 00:37:00.840 ⇒ 00:37:02.059 Uttam Kumaran: That’s insane.
322 00:37:02.060 ⇒ 00:37:09.240 James Freire: So I’m like you want to hear like I mean, like, I don’t worship the guy anything. But I was just like, I mean, it’s a type of brilliance that is just like very uncommon.
323 00:37:10.130 ⇒ 00:37:18.650 Uttam Kumaran: No, you might love this book, I can tell. You’re a reader. You should take a look at it. It may bring you back a little bit. I found this book to be
324 00:37:18.760 ⇒ 00:37:19.990 Uttam Kumaran: one of the best.
325 00:37:20.210 ⇒ 00:37:21.830 Uttam Kumaran: Do you know? Sort of like
326 00:37:22.150 ⇒ 00:37:25.740 Uttam Kumaran: business, but like business stories about
327 00:37:25.960 ⇒ 00:37:28.979 Uttam Kumaran: Sac, I mean, this is how I know everything about that company. But.
328 00:37:28.980 ⇒ 00:37:33.429 James Freire: I. I gotta read it, because, like I’m I’m scrolling right here. It says 2,008. I mean, that’s like, when I was there.
329 00:37:33.430 ⇒ 00:37:44.510 Uttam Kumaran: No, that’s why like what you were like. That’s why I was trying to recall like, did I watch something I’m like, Oh, yeah. Cause. I see the book. It’s in front of me at Black Edge. And I was like, Yeah, okay, it’s it’s great. You’ll love it.
330 00:37:44.510 ⇒ 00:37:46.030 James Freire: Check it out. Black edge.
331 00:37:48.740 ⇒ 00:37:49.899 James Freire: Yeah. Awesome.
332 00:37:50.530 ⇒ 00:37:55.170 James Freire: Okay? Well, great chatting. Yeah. I appreciate the time. And look forward to talking again soon.
333 00:37:55.170 ⇒ 00:37:57.620 James Freire: Yeah, yeah. Talk to you soon. See? You.