Meeting Title: Brainforge Interview w- Amber Date: 2026-03-18 Meeting participants: Fanu Sisay, Amber Lin
WEBVTT
1 00:02:03.030 ⇒ 00:02:04.229 Amber Lin: Hi there!
2 00:02:04.560 ⇒ 00:02:05.130 Fanu Sisay: Hello.
3 00:02:05.780 ⇒ 00:02:07.210 Amber Lin: Hi, how are you?
4 00:02:07.210 ⇒ 00:02:09.569 Fanu Sisay: I’m good. Sorry, link my volume up.
5 00:02:11.020 ⇒ 00:02:12.750 Fanu Sisay: Hi, hi, how are you?
6 00:02:12.950 ⇒ 00:02:16.350 Amber Lin: I’m good. Wait, what time zone are you based in?
7 00:02:16.420 ⇒ 00:02:18.450 Fanu Sisay: I’m in Eastern Standard Time.
8 00:02:18.450 ⇒ 00:02:21.849 Amber Lin: Oh, gosh, okay, so it’s, like, 4PM for you.
9 00:02:21.850 ⇒ 00:02:27.229 Fanu Sisay: Yeah, sun is setting right now, it’s… and it’s… it was… it’s been kind of cold, but it’s been.
10 00:02:28.110 ⇒ 00:02:29.170 Fanu Sisay: Recently.
11 00:02:29.420 ⇒ 00:02:34.449 Amber Lin: I see. I’m in… I’m in California, so it’s recently getting pretty hot.
12 00:02:34.450 ⇒ 00:02:39.359 Fanu Sisay: Sweet, yeah. Kayla let me know that a lot of you guys are out in, California.
13 00:02:39.360 ⇒ 00:02:40.170 Amber Lin: Yeah.
14 00:02:40.170 ⇒ 00:02:42.149 Fanu Sisay: Yeah, I’m jealous, right? Or at least right now
15 00:02:42.500 ⇒ 00:02:44.330 Fanu Sisay: I do love New York, but…
16 00:02:45.090 ⇒ 00:02:49.240 Fanu Sisay: this, like, you know, end of winter period where it’s still kind of cold, I definitely…
17 00:02:49.240 ⇒ 00:02:53.410 Amber Lin: I see. It was like, end of winter, it’s almost summer for me.
18 00:02:53.410 ⇒ 00:02:58.060 Fanu Sisay: Over here, it’s winter for a long, long time.
19 00:02:58.060 ⇒ 00:03:00.809 Amber Lin: I see. Wait, did you know Kayla before, or…
20 00:03:00.900 ⇒ 00:03:04.170 Fanu Sisay: No. I don’t know if she told you…
21 00:03:04.380 ⇒ 00:03:11.419 Fanu Sisay: how I got in through this process, but I actually reached out to… I want to say his name is Damilade?
22 00:03:11.420 ⇒ 00:03:11.990 Amber Lin: Yeah.
23 00:03:12.150 ⇒ 00:03:20.629 Fanu Sisay: And, I was going through the application, and there was, honestly very cool, and I was a fan of.
24 00:03:21.070 ⇒ 00:03:24.020 Fanu Sisay: Recording my own video and answering questions on.
25 00:03:24.360 ⇒ 00:03:34.350 Fanu Sisay: you know, just my personal video. But there wasn’t an insert to, you know, throw in a file of, like, whatever video I was recording. So I reached out to him, and I was like, hey,
26 00:03:34.750 ⇒ 00:03:43.550 Fanu Sisay: saw you worked at Brainforge, I can’t really send in a video, I love the idea of it, but, can’t really input it. And,
27 00:03:43.790 ⇒ 00:03:46.340 Fanu Sisay: He’s like, oh, sorry for that.
28 00:03:46.500 ⇒ 00:03:48.209 Fanu Sisay: I can put you in with our head of…
29 00:03:49.120 ⇒ 00:03:53.800 Fanu Sisay: HR, if you just want to get through to the interview process. And I was like, yeah, of course, sure.
30 00:03:53.980 ⇒ 00:04:01.850 Fanu Sisay: But it still does… it still doesn’t work. But yeah, that’s how I met Kayla and the rest.
31 00:04:02.500 ⇒ 00:04:19.070 Amber Lin: I see. Very cool. So, how this interview is gonna go, I assume this is the first, like, official interview after you talk with Kayla, so we have about 25, 30 minutes. Let’s start off with an introduction about you, about me, just a really short intro, because I do have all of
32 00:04:19.070 ⇒ 00:04:22.720 Amber Lin: Your LinkedIn, and I think Kayla’s notes.
33 00:04:22.720 ⇒ 00:04:25.860 Amber Lin: So, once we do that, I have some questions for you.
34 00:04:26.090 ⇒ 00:04:31.320 Amber Lin: And then I want to make sure to leave space at the end for you to ask me questions.
35 00:04:31.490 ⇒ 00:04:35.849 Amber Lin: Because I think you probably have quite a few that you want to ask us.
36 00:04:35.850 ⇒ 00:04:36.720 Fanu Sisay: So… Yup.
37 00:04:36.720 ⇒ 00:04:39.759 Amber Lin: Would you want to go ahead and give me a quick intro about yourself?
38 00:04:39.990 ⇒ 00:04:46.389 Fanu Sisay: Yeah, for sure. So yeah, as I mentioned, my name is Fenu, I’m based out of here in New York.
39 00:04:46.540 ⇒ 00:04:58.459 Fanu Sisay: I’ve been here for almost 5 years now, and came out here to work in the data space. I started initially working more on the backend side of things, data engineering.
40 00:04:58.650 ⇒ 00:05:09.410 Fanu Sisay: ETL, all that kind of, stuff in a consulting space, which was really nice. It was… honestly, I still kind of enjoy it, the idea of it,
41 00:05:09.500 ⇒ 00:05:19.719 Fanu Sisay: But, yeah, moved on, did a little Tableau and, Power BI data visualization work, so more front-end. And yeah, I was really able to touch on all kinds of,
42 00:05:19.720 ⇒ 00:05:31.520 Fanu Sisay: you know, data applications, different kinds of tools, which I was really thankful for. And, yeah, that’s led me to where I am now at Genesco Sports, which is a sports marketing agency.
43 00:05:31.600 ⇒ 00:05:48.939 Fanu Sisay: It’s been kind of a stark change to… I’ve been here 2 years now, it’s been a stark change to what I was doing previously, where everyone I was around was technical, and now here I am. It’s me and my boss who are the technical people, and a lot of my job is, you know, explaining tough.
44 00:05:49.130 ⇒ 00:06:07.720 Fanu Sisay: complicated concepts to people who, you know, just don’t really, or aren’t really technical or understand things. Which I enjoy, to be honest. I was a tutor growing up, so it’s kind of similar in that space. But yeah, that’s led me to where I am now, and yeah.
45 00:06:08.370 ⇒ 00:06:12.779 Amber Lin: Awesome. I think Kayla already asked you this, but why are you on the market?
46 00:06:13.150 ⇒ 00:06:28.140 Fanu Sisay: Yeah. So I definitely don’t like taking this as an opportunity to, like, bash my current employer, but, which it kind of sounds like it is. But, you know, my company’s a bit more traditional, and…
47 00:06:28.630 ⇒ 00:06:42.029 Fanu Sisay: I’ve just always enjoyed or wanted to be at the cutting edge of technology, and from, you know, what I’ve learned about Brainforge through, what I’ve seen online, the LinkedIn page, the website, it seems like that’s somewhere where I’d want to be.
48 00:06:42.710 ⇒ 00:06:48.979 Amber Lin: Oh, awesome. If you have questions about what we’re doing, I’ll make sure to answer those at the end.
49 00:06:48.980 ⇒ 00:06:49.609 Fanu Sisay: Yeah, for sure.
50 00:06:49.610 ⇒ 00:07:02.600 Amber Lin: All right, so a quick intro about myself. My name’s Amber, I joined about a year ago, and I used to work more in, like, the traditional consulting space, so I worked at UI, and then
51 00:07:02.640 ⇒ 00:07:11.349 Amber Lin: I joined this company on project management. I was recruited… I was recruited on data, but back then we… we were still…
52 00:07:12.420 ⇒ 00:07:24.659 Amber Lin: half the size of what we’re at now, and we didn’t have any delivery structures in place, so I joined on project management, and once that was more so set up, we…
53 00:07:24.870 ⇒ 00:07:28.749 Amber Lin: We started to have more of a strategy branch forming.
54 00:07:29.100 ⇒ 00:07:40.660 Amber Lin: within the last year, so that’s when I started on the strategy side, which, when I say strategy, I mean, like, data analytics, reporting.
55 00:07:40.690 ⇒ 00:07:52.240 Amber Lin: So anything after you ingest the data and model the data, we want to do reporting, analytics, opportunities, so that’s kind of where I started.
56 00:07:52.360 ⇒ 00:07:59.620 Amber Lin: In the company. So, later on, if you have questions, I can answer what the structure is currently like.
57 00:07:59.990 ⇒ 00:08:00.620 Fanu Sisay: Yeah.
58 00:08:00.620 ⇒ 00:08:05.240 Amber Lin: Alright, so I think my first question
59 00:08:05.530 ⇒ 00:08:21.150 Amber Lin: And looking at your experience, I think you worked with mostly enterprise clients, because I believe, Kayla said you were working with Pepsi and all these, like, big, big enterprise clients. Our clients’ skills are not as big.
60 00:08:21.310 ⇒ 00:08:30.030 Amber Lin: I think… The biggest we work with is around the 100 to 200 million
61 00:08:30.430 ⇒ 00:08:48.730 Amber Lin: annual revenue size, so a lot smaller than what you’ve worked with. So, I was wondering how your experience would translate to, say, smaller clients that move a lot faster, their requirements might change a lot faster, so how… what do you, what do you think there?
62 00:08:49.110 ⇒ 00:08:50.030 Fanu Sisay: Yeah, I…
63 00:08:50.500 ⇒ 00:08:57.149 Fanu Sisay: So yeah, I did talk to Kayla about Pepsi being one of our main clients, and honestly.
64 00:08:57.610 ⇒ 00:08:58.900 Fanu Sisay: I’d like to think…
65 00:08:59.180 ⇒ 00:09:15.570 Fanu Sisay: obviously, there is a huge difference, in terms of, annual revenue, the different kinds of historical things that are in place at a big company like that, but I’d say that our company, Genesco, as a whole is quite new, and…
66 00:09:15.680 ⇒ 00:09:26.350 Fanu Sisay: maybe runs a bit more in that fast pace, because we are a consultancy, we’re picking up and dropping clients quite fast. So, I actually find myself
67 00:09:26.840 ⇒ 00:09:42.170 Fanu Sisay: Introducing that kind of speed and, the more hands-on, point of view to things, when we go to our client, and we, you know, introduce as, like, hey, we know you guys have a long historical process of how you like to do things.
68 00:09:42.170 ⇒ 00:09:52.670 Fanu Sisay: But we’re definitely, you know, a new face in this, and we want to speed up things where possible, and, you know, in other places, investigate, how we can optimize.
69 00:09:52.720 ⇒ 00:10:06.269 Fanu Sisay: But, you know, I definitely feel comfortable in all kinds of capacities. Pepsi is one of the larger clients I’ve worked with, but, you know, I feel like I’ve worked with, you know, up and down the revenue scale.
70 00:10:07.400 ⇒ 00:10:18.949 Amber Lin: Okay, so I have some follow-up questions about what the typical engagement kind of looks like for you. Say, how long usually is the… is, say, a client engagement?
71 00:10:19.000 ⇒ 00:10:22.310 Fanu Sisay: Yeah, so… For, for cl…
72 00:10:22.430 ⇒ 00:10:35.939 Fanu Sisay: company like Pepsi, we’re on retainer for them, and I am, scoped out to work with them, but we also have much smaller, projects where we turn around, analysis and maybe, I’d say.
73 00:10:36.040 ⇒ 00:10:54.530 Fanu Sisay: two to three months. And honestly, those are the really fun, you know, speedy, kind of engagements we work on, where they come to us with a specific small problem, and say, hey, we know you guys have experience in the sports space, we have a commercial, and we’re looking for the best athlete that fits this, commercial.
74 00:10:54.600 ⇒ 00:11:00.179 Fanu Sisay: And those things can really… get turned around in 2 months, and I feel like that’s…
75 00:11:00.430 ⇒ 00:11:08.590 Fanu Sisay: The fun kind of stuff that we do, because with a client that we’re on retainer with, it’s kind of the same scheduled yearly work that we do for them.
76 00:11:08.790 ⇒ 00:11:23.309 Amber Lin: Gotcha, okay. So, then my question is about, say, what is the typical scope or typical topic of… of a project? Because you mentioned, for example, you would be researching what’s the best,
77 00:11:23.650 ⇒ 00:11:30.759 Amber Lin: athlete for this commercial. So, is that the main type of project you would be engaging in?
78 00:11:30.980 ⇒ 00:11:35.490 Fanu Sisay: You know, our company really presents themselves as a…
79 00:11:35.660 ⇒ 00:11:40.130 Fanu Sisay: kind of all-knowing sports entity. So…
80 00:11:40.310 ⇒ 00:11:45.620 Fanu Sisay: Some… some clients come to us with, hey, we’re looking for the best,
81 00:11:45.870 ⇒ 00:11:59.900 Fanu Sisay: you know, best athlete for a commercial. But I’d say that’s just something that I, you know, comes to the top of my head. Another thing is, like, hey, we have billboards all throughout, this stadium, and they show up on TV.
82 00:11:59.950 ⇒ 00:12:16.440 Fanu Sisay: X amount of time, and we notice that other billboards are showing up more. How can we optimize our placements, in a stadium? You know, I’d say things range, but all throughout the world of sports marketing, so social media, optimizing,
83 00:12:16.680 ⇒ 00:12:22.440 Fanu Sisay: Partnered, posts, campaigns that people run online,
84 00:12:23.170 ⇒ 00:12:28.899 Fanu Sisay: scheduling, like, rostering for a TV commercial, all kinds of things in the sports space.
85 00:12:29.210 ⇒ 00:12:44.379 Amber Lin: Gotcha. It sounds like, in terms of domain and not industry, it sounds like you’ve worked with a lot of marketing, analysis and reporting. Is that the only industry you’ve worked in? We have a lot of, like, marketing-focused clients, so…
86 00:12:44.380 ⇒ 00:12:44.970 Fanu Sisay: Yeah.
87 00:12:44.970 ⇒ 00:12:50.179 Amber Lin: you would fit there, I just want to know, like, what other domains you’ve worked with.
88 00:12:50.180 ⇒ 00:12:58.579 Fanu Sisay: Yeah, definitely the last two years has been a lot of marketing, sports marketing, fully. I, you know.
89 00:12:58.730 ⇒ 00:13:12.599 Fanu Sisay: I do really look back at my time and my first, consultancy that I worked at Curis, where I did a lot of work with data engineering. I found that to be enjoyable. To be honest, I consider myself quite flexible.
90 00:13:13.060 ⇒ 00:13:23.439 Fanu Sisay: you know, we… we do a lot of marketing, but we also do a lot of strategizing for the future of, Pepsi, and we know that they want to get into the,
91 00:13:23.980 ⇒ 00:13:27.710 Fanu Sisay: I forgot what they’re called, the sodas that have,
92 00:13:28.080 ⇒ 00:13:37.959 Fanu Sisay: the prebiotic sodas. They want to get into that space, and they, you know, use sports as a tool to get into it, but…
93 00:13:38.200 ⇒ 00:13:48.329 Fanu Sisay: I think it’s also a lot of strategy alongside with marketing, but yeah. I feel comfortable in marketing, but I think I’m open to all kinds of things. I hope I answered your question there.
94 00:13:48.530 ⇒ 00:13:56.469 Amber Lin: Yeah, I… I think judging from your experience, I knew you were open to different things, but it’s good to hear from you as well. So…
95 00:13:57.100 ⇒ 00:14:15.270 Amber Lin: Essentially, I’m trying to note down the domain and industry. So you’re saying domain, mainly marketing, and touch some other domains when you were working as a DE, and then in terms of industry, would that mainly be sports as well, or would that be, like, a
96 00:14:15.350 ⇒ 00:14:17.899 Amber Lin: Do you have other industry experiences?
97 00:14:18.150 ⇒ 00:14:24.390 Fanu Sisay: Yeah, so, in terms of the industry, at my first company, we worked a lot in,
98 00:14:25.040 ⇒ 00:14:29.860 Fanu Sisay: Nonprofit, CPG, brands.
99 00:14:29.860 ⇒ 00:14:30.740 Amber Lin: Okay.
100 00:14:30.740 ⇒ 00:14:36.239 Fanu Sisay: So, yeah, I, like, we definitely use sports as a tool to, you know.
101 00:14:36.450 ⇒ 00:14:45.910 Fanu Sisay: get work with these clients, but I think we do all kinds of stuff. Another big client was a pharmaceutical client that I worked with at my first stop.
102 00:14:46.560 ⇒ 00:14:58.910 Amber Lin: Gotcha. Okay, awesome. So, we have some… I think our clients are mainly CPG, pharmacy, and some SaaS, and a little bit of service.
103 00:14:58.910 ⇒ 00:15:15.050 Amber Lin: Okay. So that’s our main, like, industry exposure, which I think aligns pretty well. For the domains, I actually had a question about your DE work, because I know for your current company, you said only your boss and you are technical, so…
104 00:15:15.230 ⇒ 00:15:24.680 Amber Lin: Do you work with the client’s data engineering team, or how do you get the data to do the reporting-type work you need to do?
105 00:15:24.680 ⇒ 00:15:28.979 Fanu Sisay: Yeah, so we have a lot of vendors that we work with, as a…
106 00:15:29.260 ⇒ 00:15:32.670 Fanu Sisay: as a company on our side. And then, we also know that
107 00:15:33.380 ⇒ 00:15:39.960 Fanu Sisay: you know, depending on the client and how much they’re invested within data, they have, sources themselves, so…
108 00:15:40.310 ⇒ 00:15:54.360 Fanu Sisay: I think we work hand-in-hand, and it obviously varies between clients. Some, you know, keep their sales data very close to their heart and aren’t willing to share, and then others, it’s, you know, an open book.
109 00:15:54.860 ⇒ 00:15:56.030 Fanu Sisay: But yeah,
110 00:15:56.690 ⇒ 00:16:10.009 Fanu Sisay: I’d say we have sources ourselves, vendors, different kinds of databases that we’re able to connect to, and then we also rely for specific clients on the data they have in store themselves.
111 00:16:10.010 ⇒ 00:16:28.079 Amber Lin: Gotcha, okay. And then for your current company, is it mostly, say, consulting style? We do this research, we present it in a presentation, or is it a lot of, we built this report with… for you with all these KPIs that’s on this dashboard that you can access? Like, what type of work is it… is it mainly?
112 00:16:28.350 ⇒ 00:16:34.400 Fanu Sisay: Yeah, I… I think it’s best I, like, focus on one client and explain.
113 00:16:34.400 ⇒ 00:16:34.800 Amber Lin: Okay.
114 00:16:34.800 ⇒ 00:16:42.139 Fanu Sisay: Yeah, we do work on a couple of clients, and I feel like I’m saying that every time I answer. But yeah, for,
115 00:16:42.300 ⇒ 00:16:47.340 Fanu Sisay: for… for example, Pepsi, as we mentioned earlier, we do…
116 00:16:47.570 ⇒ 00:16:55.210 Fanu Sisay: everything across the board. So we have simple reports that we run, that, you know, are really just handing off at their door.
117 00:16:55.210 ⇒ 00:17:11.579 Fanu Sisay: adding some, you know, some of our insights, but really for them to pass along the company and say internally, hey, this is how we’re performing in this specific, league, in this specific, you know, campaign, that’s how we’re performing. There’s also a lot of
118 00:17:11.579 ⇒ 00:17:20.730 Fanu Sisay: other work streams that, you know, are more of a… we’re building as we go. For example, we’re doing a model right now that,
119 00:17:20.730 ⇒ 00:17:38.929 Fanu Sisay: you know, considers all kinds of properties, sports, or leagues, teams, athletes, all on the same playing field, and they’re able to recognize, oh, who is the best fit for us? That is something that we’re constantly changing. It’s a work stream that, you know, I’d say is separate from our
120 00:17:38.990 ⇒ 00:17:53.050 Fanu Sisay: casual consulting, space. And that is, you know, I wouldn’t call that work stream something that is, like, a simple drop-off report. We’re constantly adding, new features to it,
121 00:17:53.270 ⇒ 00:17:55.290 Fanu Sisay: Yeah, I don’t know if that answered yet.
122 00:17:55.290 ⇒ 00:18:10.049 Amber Lin: I see. So, would you… do you guys also do the modeling and the, say, more on the data engineering work? Or, I guess, sorry, specifically for you, do you also do, like, data engineering work at this current company, or just, like.
123 00:18:10.050 ⇒ 00:18:10.660 Fanu Sisay: Yeah.
124 00:18:10.660 ⇒ 00:18:12.839 Amber Lin: it for you, and then you do the reports.
125 00:18:12.960 ⇒ 00:18:19.719 Fanu Sisay: Yeah, very, very loosely, I would say. We work with, you know, different kinds of ETL tools, but…
126 00:18:19.720 ⇒ 00:18:33.810 Fanu Sisay: I definitely wouldn’t say that it’s… I have a team of engineers with me that are building it out. It’s something that will do simple workflows, just to build out specific data streams. We’ll have
127 00:18:34.310 ⇒ 00:18:35.150 Fanu Sisay: you know.
128 00:18:36.170 ⇒ 00:18:44.770 Fanu Sisay: we’ll have data on a specific database that we need to transform, load it into our model, and, you know, those things I…
129 00:18:45.600 ⇒ 00:18:51.780 Fanu Sisay: Yeah, I guess I would call it data engineering. I just, like, when I think of data engineering, I think of a team of, you know, four with.
130 00:18:51.780 ⇒ 00:18:52.430 Amber Lin: Yeah.
131 00:18:52.430 ⇒ 00:18:56.160 Fanu Sisay: a lead consultant, a PM, you know, like, a much more…
132 00:18:56.160 ⇒ 00:18:58.049 Amber Lin: Your first company, like, what it looks like.
133 00:18:58.050 ⇒ 00:19:01.179 Fanu Sisay: It’s much more casual, because it’s me and my boss that handle…
134 00:19:01.180 ⇒ 00:19:01.730 Amber Lin: culture.
135 00:19:01.730 ⇒ 00:19:02.300 Fanu Sisay: Yeah.
136 00:19:04.670 ⇒ 00:19:08.120 Amber Lin: Noting that down…
137 00:19:08.640 ⇒ 00:19:28.480 Amber Lin: Would you touch a little bit upon, like, your DE exposure at your first company? Because we work very close. We do have a team of DEs, and then we have a team of data analysts, so we work… we do have a DE team, that’s what you would call it, so I want to hear your… kind of your experience working in that area.
138 00:19:28.790 ⇒ 00:19:33.879 Fanu Sisay: Yeah, so, I was an analyst, or I guess, yeah, I turned into a consultant on,
139 00:19:33.990 ⇒ 00:19:48.869 Fanu Sisay: a couple of engagements, really just building out, ETL flows that, you know, extracted from a singular data source. I think most of the time I was working with, Snowflake, sometimes Microsoft SQL Server.
140 00:19:48.950 ⇒ 00:20:08.230 Fanu Sisay: And we had a tool called Rivery, very similar to something like Alteryx or Talent, where we were pulling from a specific source, conducting all of our transformations, our data quality checks, in, you know, 3 different stages, and pushing that all the way to,
141 00:20:08.360 ⇒ 00:20:11.340 Fanu Sisay: you know, production. And I, you know.
142 00:20:11.940 ⇒ 00:20:24.659 Fanu Sisay: really enjoyed it. It, you know, wasn’t the best fit for me moving forward. I was really happy when I got this role, and, you know, it was like, okay, I’m working in sports now. But I miss the technical…
143 00:20:24.860 ⇒ 00:20:33.690 Fanu Sisay: everydayness of what I was doing there, a lot. And, you know, it makes it sound like I’m, like, bouncing back and forth, but, you know, I…
144 00:20:34.490 ⇒ 00:20:36.490 Fanu Sisay: I considered it fun.
145 00:20:37.310 ⇒ 00:20:39.810 Fanu Sisay: And I think I would leave it at that.
146 00:20:40.230 ⇒ 00:20:47.270 Amber Lin: Yeah, honestly, I think people do that. I think for… in our company, I think…
147 00:20:47.550 ⇒ 00:21:02.569 Amber Lin: one of the recent… not recent hires, so he… he was a philosophy professor, and then he went to do product analytics. So, I think hopping around is… is very normal, and honestly gives you more exposure and understanding of different fields.
148 00:21:02.570 ⇒ 00:21:12.489 Fanu Sisay: Yeah. You had mentioned that you started at Brainforge as a PM. I might have missed it. What avenue of work do you do for Brainforge?
149 00:21:12.910 ⇒ 00:21:32.769 Amber Lin: So, I currently am on the strategy team, so I do this, essentially kind of like what you do now, so reporting, building reports with different clients, and sometimes also consulting of, okay, this is… you want to go in here, what’s the opportunity size, here’s some experiments you can run, so, like, a combination of that.
150 00:21:32.860 ⇒ 00:21:36.010 Fanu Sisay: And when you say strategy, I, like…
151 00:21:36.010 ⇒ 00:21:53.720 Fanu Sisay: for example, we just had a big talk at our current company, like, saying, like, what should we title our division? It was research and insights before we moved to data analytics, and then now they want to call us data and strategy. Like, what would you consider your title of strategy?
152 00:21:54.340 ⇒ 00:22:05.300 Amber Lin: I’ll call it data and strategy, because from a company perspective, it feels it is more empowering and more impactful if we’re not just…
153 00:22:05.330 ⇒ 00:22:21.490 Amber Lin: say, researching for our clients, but instead of taking that and putting our own interpretation and synthesis on, hey, this is what it means, this is why you should look at it, and why you maybe shouldn’t look at something else.
154 00:22:21.660 ⇒ 00:22:21.980 Fanu Sisay: No.
155 00:22:21.980 ⇒ 00:22:25.389 Amber Lin: I will call it a strategy, because we’re really advising them, and
156 00:22:25.680 ⇒ 00:22:29.839 Amber Lin: Not just doing a task that they assign us.
157 00:22:29.840 ⇒ 00:22:35.610 Fanu Sisay: Yeah, I think that was the issue when I first started, you know, working, like, I felt like…
158 00:22:35.710 ⇒ 00:22:51.549 Fanu Sisay: you know, all these people I’m talking to are much older than me, they don’t want to consider what I think about, specific stuff I’m sending to them, and then my manager would, you know, just kind of say, like, hey, you know, we’re… we need your insights on the data that you’re looking at, because…
159 00:22:52.020 ⇒ 00:22:56.569 Fanu Sisay: Fairly… no one is gonna look at it as much as the person who’s pulling and, you know.
160 00:22:56.700 ⇒ 00:23:02.099 Fanu Sisay: seeing the data first, that’s likely the person who’s gonna spend the most time with it. So, it’s important.
161 00:23:02.450 ⇒ 00:23:03.130 Fanu Sisay: Yeah.
162 00:23:03.430 ⇒ 00:23:16.739 Amber Lin: Yeah, my last question is, what… so what was, like, the ownership structure in your current company? Did your boss assign you tasks, or did the client…
163 00:23:16.740 ⇒ 00:23:24.939 Amber Lin: Did the client have clear requirements, or did you have to own, like, a work stream? I’m just trying to understand, like, what was the…
164 00:23:25.000 ⇒ 00:23:29.400 Amber Lin: What’s the level of responsibility that you have at your current company?
165 00:23:29.720 ⇒ 00:23:32.730 Fanu Sisay: Yeah, I would say… you know.
166 00:23:33.130 ⇒ 00:23:52.219 Fanu Sisay: it being… so, my manager and I were both based in New York. We both go to the office quite often, so it was very casual in the way that I was moved into all of our clients. You know, I was introduced initially and was really just taking notes, getting a good grasp of things.
167 00:23:52.290 ⇒ 00:23:59.300 Fanu Sisay: And over time, I would take smaller and smaller clients, in its entirety, and would work with the client,
168 00:23:59.400 ⇒ 00:24:07.940 Fanu Sisay: you know, strictly with the client. Not a lot of check-ins with my boss, really just a, oh, how are things there? Yep, good, okay, sweet, move on.
169 00:24:07.940 ⇒ 00:24:21.960 Fanu Sisay: Obviously with our bigger clients, my boss has a lot more input in what we do, and the projects, I think, are much more… you know, they’re things he wants his input on, as well.
170 00:24:22.550 ⇒ 00:24:29.909 Fanu Sisay: I still think there are… like, I still feel comfortable with the fact that I’m able to handle work streams all by myself,
171 00:24:30.140 ⇒ 00:24:34.879 Fanu Sisay: But, you know, the bigger clients, my boss cares a lot about, so he…
172 00:24:34.880 ⇒ 00:24:35.440 Amber Lin: I see.
173 00:24:35.440 ⇒ 00:24:37.169 Fanu Sisay: He ends up having a lot of input there.
174 00:24:37.400 ⇒ 00:24:47.640 Amber Lin: Gotcha. So for the workstreams that you own, is it just you working on them, or do you also talk with, say, the non-technical people in your company?
175 00:24:47.640 ⇒ 00:24:51.399 Fanu Sisay: For every client, we have an account team that’s built out,
176 00:24:51.600 ⇒ 00:25:07.990 Fanu Sisay: usually someone who’s much more senior, who’s kind of an overlooker, and then a day-to-day, you know, check-in. I’m usually constantly in conversation with that day-to-day check-in. There are spaces and specific clients where, really, I’m just talking with someone on the client side.
177 00:25:07.990 ⇒ 00:25:17.570 Fanu Sisay: My… you know, there’s someone who usually brings that client in that will check in with me and say, hey, they had mentioned that you’re working on
178 00:25:17.610 ⇒ 00:25:33.609 Fanu Sisay: so-and-so, or such and such for them. Just wanted to make sure deadlines are good there, and that is really the majority of the input they request there. But yeah, every client is different, so it’s tough to put one strict answer on it.
179 00:25:33.610 ⇒ 00:25:43.589 Amber Lin: Oh, I see. I think I was asking more of… for the work that you had to do. Is it just you on the work stream, or did you have a team? You had to do it together?
180 00:25:43.600 ⇒ 00:25:44.770 Fanu Sisay: Oh, okay, I, yeah.
181 00:25:44.770 ⇒ 00:25:45.740 Amber Lin: your team.
182 00:25:45.740 ⇒ 00:25:54.739 Fanu Sisay: Yeah, I apologize. Yeah, I have one analyst, working under me, and he’s started within the last couple months, so he’s definitely still…
183 00:25:55.390 ⇒ 00:25:56.130 Fanu Sisay: you know.
184 00:25:56.360 ⇒ 00:26:01.289 Fanu Sisay: kicking up on his, amount of work he’s doing, but, yeah, I have one analyst under me.
185 00:26:01.860 ⇒ 00:26:09.230 Amber Lin: I see. And what’s… do you review his work? Do you assign work to him, or what’s the type of relationship you guys have?
186 00:26:09.460 ⇒ 00:26:14.039 Fanu Sisay: Yeah, I think that’s something my boss and I are still kind of building through.
187 00:26:14.220 ⇒ 00:26:18.079 Fanu Sisay: you know, it’s within the first couple months. I’ve… I…
188 00:26:18.400 ⇒ 00:26:34.239 Fanu Sisay: it’s funny, because it’s really just, like, location politics. Like, he sits next to me, so I’m constantly in conversation with him, and I give him direct orders, but, my boss will do a lot of, like, overseeing, like, oh, hey, are you busy with stuff that FNU has given you?
189 00:26:34.240 ⇒ 00:26:35.020 Amber Lin: It’s not clear.
190 00:26:35.020 ⇒ 00:26:42.999 Fanu Sisay: please assist here. But… you know, I would say I’m his day-to-day handling, of his…
191 00:26:43.430 ⇒ 00:26:44.419 Fanu Sisay: Of his work.
192 00:26:44.950 ⇒ 00:26:51.100 Amber Lin: Gotcha. Awesome. It took a little bit more time, so I’ll answer, all the questions you have, so…
193 00:26:51.100 ⇒ 00:26:58.239 Fanu Sisay: Yeah, I do have a lot of questions. I’ll cut it down, but, okay,
194 00:26:58.610 ⇒ 00:27:12.629 Fanu Sisay: My first one is, you know, from my understanding, Brainforge is an AI company, and I was just curious how you guys use AI. Is that something someone in this role would be using constantly? In what kind of capacities? Yeah, just curious about that.
195 00:27:13.020 ⇒ 00:27:17.800 Amber Lin: Gotcha. So, our company, I would say, is the… Most…
196 00:27:18.190 ⇒ 00:27:34.030 Amber Lin: that has the most robust and thorough AI usage I’ve seen. So, to give you a context, we recently unsubscribed from ChatGPT Pro because everybody in our company is now using Cursor, which can do a lot more things. Yeah. So…
197 00:27:34.790 ⇒ 00:27:37.149 Amber Lin: Every part of our company
198 00:27:38.050 ⇒ 00:27:57.429 Amber Lin: tries to use AI, or is currently being automated to be enhanced with AI. So, starting from, say, sales, and generating posts, and generating outreach, to marketing, to small workflows that help HR, and then to our delivery team, which,
199 00:27:57.530 ⇒ 00:28:16.960 Amber Lin: Our company is 3 main branches of data, strategy, and then AI. And data kind of supports both strategy and AI. And currently, all these roles, of course, AI uses their own AI workflows, and then data and strategy is more and more starting to use
200 00:28:17.350 ⇒ 00:28:21.209 Amber Lin: AI in our daily work. So, as an example.
201 00:28:21.340 ⇒ 00:28:31.700 Amber Lin: for the data team, so when they go and check, let’s say they want to update this model, they work in Kurser to say, okay, I want to update these fields.
202 00:28:31.700 ⇒ 00:28:49.260 Amber Lin: It goes and looks at our dbt and all these models and definitions and say, okay, you can update it there. And then we… I think recently we had a workflow to update data documentation, because that’s usually what people don’t like to do. So that workflow will take a look at all our models and
203 00:28:49.390 ⇒ 00:28:54.919 Amber Lin: Where they come from, and what they are, and update it to our data… data documentation.
204 00:28:54.970 ⇒ 00:28:55.670 Fanu Sisay: Okay.
205 00:28:55.670 ⇒ 00:29:03.590 Amber Lin: And for my work, so for my strategy work, I actually started completely with Wow.
206 00:29:03.740 ⇒ 00:29:06.679 Amber Lin: with AI in analysis, so…
207 00:29:07.430 ⇒ 00:29:14.060 Amber Lin: excluding the work I did before this company, the analysis work I did here is all
208 00:29:14.490 ⇒ 00:29:20.460 Amber Lin: done within the cursor client, so… for example, I’m connected to
209 00:29:20.790 ⇒ 00:29:32.039 Amber Lin: Snowflake is sometimes BigQuery, depending what it is, and then I’m able to use that to run EDAs, basic explorations, and then do…
210 00:29:32.350 ⇒ 00:29:42.990 Amber Lin: analysis, or… I think more like the stuff that you’re doing right now of, okay, this is a problem that… or question the client has.
211 00:29:43.070 ⇒ 00:29:59.270 Amber Lin: This is an experiment that they ran, what was the results? For example, we can look at lifecycle, or we can look at, conversion, time to conversion, NCAC, like, those type of things. I can also do it in Kurser.
212 00:29:59.490 ⇒ 00:30:00.060 Fanu Sisay: Yeah.
213 00:30:00.060 ⇒ 00:30:06.910 Amber Lin: And the work stream, all more on the reporting side, which is my current work this week.
214 00:30:07.290 ⇒ 00:30:18.470 Amber Lin: We are using a more AI-native reporting tool, it’s called Omni, so it’s essentially the same as Tableau Power BI, but they have a very integrated
215 00:30:18.610 ⇒ 00:30:25.480 Amber Lin: AI, System that helps clients self-serve and ask questions, and helps us
216 00:30:25.630 ⇒ 00:30:40.630 Amber Lin: build reports if we give it the right model data. So I’m working with that very closely. So, overall, to answer your question, yes, we’re very integrated, and we want to be as integrated as possible.
217 00:30:41.140 ⇒ 00:30:44.700 Fanu Sisay: Yeah, that’s great to hear. That’s actually…
218 00:30:44.900 ⇒ 00:30:50.639 Fanu Sisay: I feel another pain point. I don’t know if I fully mentioned that earlier, but, I…
219 00:30:51.380 ⇒ 00:30:56.999 Fanu Sisay: another… I don’t want to say reasons I want to leave my company is… you know, I… I…
220 00:30:58.070 ⇒ 00:31:14.250 Fanu Sisay: I’ve used AI a lot recently over the last 2 or 3 years, and I think it would be really helpful for our company to have, you know, more memberships across the teams. And I built a huge presentation with AI, and it was rejected, and…
221 00:31:14.650 ⇒ 00:31:26.339 Fanu Sisay: you know, they just felt it wasn’t necessary at the time, and, you know, I disagreed, and it’s made me consider, you know, other places to go. So it’s great to hear that, you know, Brainforge is hard at work.
222 00:31:26.560 ⇒ 00:31:30.520 Fanu Sisay: Yeah, I guess another main question I have is…
223 00:31:31.370 ⇒ 00:31:35.720 Fanu Sisay: your role specifically. So, you’ve been at Brain Forge for a couple years now?
224 00:31:35.980 ⇒ 00:31:36.639 Fanu Sisay: if I…
225 00:31:36.640 ⇒ 00:31:37.800 Amber Lin: Just one year.
226 00:31:37.800 ⇒ 00:31:38.490 Fanu Sisay: Okay.
227 00:31:38.920 ⇒ 00:31:54.380 Fanu Sisay: How have you felt that, like, growing at the company is, and how do you consider yourself, or someone in this role? Is there any kind of stunted growth, or do you feel freedom to, you know, move up at this company?
228 00:31:55.360 ⇒ 00:32:01.510 Amber Lin: Yeah, I actually felt, that growth is a very…
229 00:32:01.620 ⇒ 00:32:11.879 Amber Lin: encouraged and very easy thing to do. The only thing limiting is my ability. Not that they will limit me, but I just… I can’t do it yet.
230 00:32:12.090 ⇒ 00:32:14.190 Amber Lin: For example.
231 00:32:14.330 ⇒ 00:32:25.209 Amber Lin: When I started, I was… I had zero experience in project management, but, eventually I was able to help us set up the PMO and see, okay, what is…
232 00:32:25.340 ⇒ 00:32:44.269 Amber Lin: what are possibilities for us to do? And then on the data analyst role, I also didn’t have the full type of analyst exposure. I had consulting experience, but I didn’t have, say, working with models and working with, putting this in a dashboard.
233 00:32:44.450 ⇒ 00:32:49.189 Amber Lin: So, I essentially learned that from scratch, and
234 00:32:49.520 ⇒ 00:32:55.149 Amber Lin: Well, I do think we are very fast-paced, Because we’re a consulting company.
235 00:32:55.330 ⇒ 00:33:06.409 Amber Lin: Overall, there is freedom if you want to say, I want to grow in more on the sales side, or I want to become a service lead that’s super…
236 00:33:07.000 ⇒ 00:33:13.070 Amber Lin: experience and technical on overseas and workflow branch, there’s always opportunities.
237 00:33:13.760 ⇒ 00:33:26.850 Amber Lin: To grow in that way, and it’s encouraged, because we’re always in short of people who can, say, lead an engagement, or lead a group of people, or, say, architect out,
238 00:33:27.230 ⇒ 00:33:34.150 Amber Lin: complex problems when we interface with clients. So, to answer your question, I think growth is…
239 00:33:34.320 ⇒ 00:33:44.469 Amber Lin: Unlimited, as long as you have a certain direction you want to go, and find opportunities to develop it.
240 00:33:45.930 ⇒ 00:33:52.949 Fanu Sisay: Yeah, sweet. Okay. Yeah, that’s great. I don’t want to hold you too long. Do you have time for one more question?
241 00:33:52.950 ⇒ 00:33:57.350 Amber Lin: I think I have one more question. Yes, I have a call, but I just told him I would be 5 minutes late.
242 00:33:57.470 ⇒ 00:33:59.640 Fanu Sisay: I’m sorry, yeah.
243 00:33:59.820 ⇒ 00:34:13.189 Fanu Sisay: Okay, yeah, this last question is just, what are your thoughts on the people you work with? Are you in constant combo with people at Brainforge, or are you mainly talking to clients? Yeah, I was just wondering what that was like.
244 00:34:13.969 ⇒ 00:34:30.569 Amber Lin: Gotcha. We are a remote company, but we do talk a lot within ourselves, especially within our individual teams. I think as the company got harder, it got bigger, it was hard to talk with everybody in the company, but say, for example, on our strategy team, or with
245 00:34:30.639 ⇒ 00:34:36.969 Amber Lin: data engineers that I work with. We do talk pretty often, and we usually just
246 00:34:37.049 ⇒ 00:34:44.629 Amber Lin: call people on Slack or schedule meetings, and then we always message back and forth. So, communication’s really good.
247 00:34:44.759 ⇒ 00:34:48.919 Amber Lin: Yeah. And I think another important part is that people are very helpful.
248 00:34:48.969 ⇒ 00:35:08.079 Amber Lin: I recently, when I was setting up the dashboards with this new AI workflow, I had no clue what I was doing, so I had to call someone and say, please help me. And it was a very tight timeline. He had his own stuff to do, but he was willing to say, hey, let me call you right now, let me walk you through, so we spent 30 minutes.
249 00:35:08.079 ⇒ 00:35:10.899 Amber Lin: And then I was able to do my work that day.
250 00:35:11.079 ⇒ 00:35:15.849 Amber Lin: So, I think people at this company are great. I think…
251 00:35:16.509 ⇒ 00:35:21.739 Amber Lin: As we hired, we’ve been able to select and then retain,
252 00:35:22.359 ⇒ 00:35:34.929 Amber Lin: kind people, that’s also smart, that’s also collaborative, so I like the people that I work with, so… and I hope, like, if you were to join us, I think you’ll like them too.
253 00:35:35.180 ⇒ 00:35:39.200 Fanu Sisay: Yeah, no, that sounds great, and definitely an environment.
254 00:35:39.230 ⇒ 00:35:58.319 Fanu Sisay: I feel like I would enjoy. I saw a low ego collaborator on the job description, and that just felt like a unique word that I fully agree with and was happy to read. But yeah, I don’t want to hold you any longer, I just wanted to say thanks again for taking the time to meet with me. I really do appreciate it.
255 00:35:58.690 ⇒ 00:36:11.779 Amber Lin: Awesome! I really enjoyed the conversation, so thank you for the time. The next steps, I think operations will get back to you within a week or two, no matter what the decision is, and they’ll follow up with the next steps.
256 00:36:11.780 ⇒ 00:36:13.579 Fanu Sisay: Awesome, thank you so much. Have a good day, Amber.
257 00:36:13.580 ⇒ 00:36:14.749 Amber Lin: Alright, me too.
258 00:36:15.320 ⇒ 00:36:16.080 Amber Lin: Bye.