Meeting Title: Brainforge Interview w- Amber Date: 2026-02-11 Meeting participants: Amber Lin, Advait Nandakumar Menon
WEBVTT
1 00:00:23.510 ⇒ 00:00:25.370 Amber Lin: Hi there, how are you?
2 00:00:25.540 ⇒ 00:00:27.000 Advait Nandakumar Menon: Hello, how are you?
3 00:00:27.290 ⇒ 00:00:29.070 Amber Lin: I’m good! How are you doing?
4 00:00:29.570 ⇒ 00:00:31.079 Advait Nandakumar Menon: I’m doing good, how are you?
5 00:00:31.830 ⇒ 00:00:37.250 Amber Lin: I’m good, it’s about lunchtime, so… just had lunch. What about you? What time is it for you?
6 00:00:37.690 ⇒ 00:00:43.050 Advait Nandakumar Menon: It’s 3PM for me, so I am in the EST time zone, so… Hmm. Yeah.
7 00:00:43.740 ⇒ 00:00:44.870 Amber Lin: Cool, awesome.
8 00:00:45.010 ⇒ 00:00:52.459 Amber Lin: So this is your first interview, and you’re interviewing for the digital product analyst role, right?
9 00:00:52.930 ⇒ 00:01:00.859 Advait Nandakumar Menon: Right. I specifically applied to the, Senior Associate for, Data and Insights role, so, yeah.
10 00:01:01.710 ⇒ 00:01:03.910 Amber Lin: Gotcha, okay.
11 00:01:05.010 ⇒ 00:01:15.590 Amber Lin: All right, let’s start off, with a quick introduction. So, my name is Amber, I’ve been at Brainforge for about a year, and I work on… more on the strategy.
12 00:01:15.590 ⇒ 00:01:26.740 Amber Lin: on the analytics side. So, we’d love to hear a quick intro about you, and then I’ll have some questions, and I’ll make sure to leave space for you to ask questions as well.
13 00:01:26.740 ⇒ 00:01:27.320 Advait Nandakumar Menon: Sure.
14 00:01:27.640 ⇒ 00:01:45.349 Advait Nandakumar Menon: Well, nice to meet you, Amber. So, I’m Advait. I have around four and a half to five years of experience in BI, data analytics, and engineering, and consulting overall. So, I started at TCS, building dashboards and working on customer analytics.
15 00:01:45.350 ⇒ 00:01:58.009 Advait Nandakumar Menon: Then moved into a data engineering role, where I worked on pipelines, migrations, and performance improvements. Most recently, though, I was working as a data analyst at a consulting startup.
16 00:01:58.010 ⇒ 00:02:06.289 Amber Lin: So, there I was supporting multiple clients across, insurance, SaaS, manufacturing, and even some non-profit organizations, so…
17 00:02:06.290 ⇒ 00:02:22.420 Advait Nandakumar Menon: My focus here mainly was on taking unclear business requirements, turning them into something teams can actually use, such as dashboards, automations, and reporting that basically helps teams make better decisions, in my opinion, so…
18 00:02:22.560 ⇒ 00:02:39.420 Advait Nandakumar Menon: a big part of what I really enjoy is making things that people actually use, and it gets adopted. So, for example, making a dashboard or report that just doesn’t sit there for analysis, but it drives the decision overall in their core.
19 00:02:39.500 ⇒ 00:02:54.769 Advait Nandakumar Menon: So, yeah, really, I’m going forward, I want to keep growing in those kinds of roles, and I want to really take ownership of such tasks and work end-to-end closely with stakeholders, and this opportunity at Brainforge really stands out to me.
20 00:02:55.190 ⇒ 00:03:03.000 Amber Lin: Okay, awesome. So… I think in this interview, I wouldn’t dive too deep into technical questions.
21 00:03:03.000 ⇒ 00:03:03.360 Advait Nandakumar Menon: I wouldn’t.
22 00:03:03.360 ⇒ 00:03:17.799 Amber Lin: give you, like, practice tests, etc. So my main goal is to understand who you are, and for you to also understand who we are, so it’s always a mutual fit. So, my first question, you mentioned it briefly already, is…
23 00:03:17.800 ⇒ 00:03:21.760 Amber Lin: What would motivate you in work? Because there’s…
24 00:03:21.760 ⇒ 00:03:38.319 Amber Lin: I’ve talked to many of my colleagues, and I’ve talked to different people. Some people like the people aspect, some people like the learning aspects, some people, their goal is to earn more compensation throughout the career ladder. So I wanted to learn, like, what is important for you.
25 00:03:39.010 ⇒ 00:03:48.139 Advait Nandakumar Menon: Yeah, that’s a good question. So, for me, motivation is when I would consider, like, seeing a clear impact from my work.
26 00:03:48.140 ⇒ 00:04:01.810 Advait Nandakumar Menon: as I mentioned earlier, like, when something I build saves people, like, their time, improves visibility, or really helps them make a better decision, that’s when that work feels fulfilling and meaningful to me.
27 00:04:01.810 ⇒ 00:04:20.380 Advait Nandakumar Menon: I also enjoy the problem-solving aspect, like, especially working as a consultant. You are often dealt with messy data sets or unclear requirements at the start, so I like taking something, like, unclear or vague, structure it really, and then slowly turn it into something usable for
28 00:04:20.380 ⇒ 00:04:25.869 Advait Nandakumar Menon: the end clients. So, that’s what really motivates me, as a consultant and analytics.
29 00:04:27.050 ⇒ 00:04:42.120 Amber Lin: Gotcha, okay, sounds good. And then my question was, you mentioned you worked in analytics, you were more on the dashboard side, and then you did some DE work, and then you were more like a consultant, so I was wondering where… what type of…
30 00:04:42.240 ⇒ 00:04:56.899 Amber Lin: career do you want to do? So, like, what do you see in the future you would be doing? Is there something you want to become, or something you want to learn more? Or just, what do you see as the future of your work?
31 00:04:58.160 ⇒ 00:05:00.319 Advait Nandakumar Menon: Oh… from a…
32 00:05:00.450 ⇒ 00:05:16.630 Advait Nandakumar Menon: Carrier perspective, right now, like, currently I’m looking for roles where I can grow and work directly on the real business problems and own the work streams end-to-end, not just deliver something for the sake of it and just be hands-off in.
33 00:05:17.130 ⇒ 00:05:32.390 Advait Nandakumar Menon: So, the past few years, I have worked with a n number of clients, building strong technical analytics foundations, but like I said, I’ve enjoyed being really close to the decision-making process, so collaborating and working with stakeholders.
34 00:05:32.390 ⇒ 00:05:37.400 Advait Nandakumar Menon: Translating the unclear requirements into something that can be really structured and
35 00:05:37.510 ⇒ 00:05:46.749 Advait Nandakumar Menon: people overall use it in their daily lives. So, over the next few years, if you ask me, I want to keep working on that and deepen my expertise, like.
36 00:05:46.800 ⇒ 00:06:00.809 Advait Nandakumar Menon: become really someone who takes even more ownership, like taking a messy problem and building the analysis or system around it, and really drive systems and, I mean, teams to actually act on it. So.
37 00:06:00.810 ⇒ 00:06:08.709 Advait Nandakumar Menon: that would be a short, look into my career going ahead. So, really being an owner, in that aspect.
38 00:06:10.250 ⇒ 00:06:26.170 Amber Lin: I see, so I’m trying to understand a bit more there. You were mentioning ownership, you’re mentioning translating requirements, so are you talking more about, say,
39 00:06:26.270 ⇒ 00:06:30.420 Amber Lin: Like, managing a team of analysts, or…
40 00:06:30.480 ⇒ 00:06:41.490 Amber Lin: Becoming more, like, an account manager and managing relationships with a client directly? Or it sounds like a more technical project manager role?
41 00:06:41.510 ⇒ 00:06:51.670 Amber Lin: Or say, are you becoming a very technical person and advising people on what to do? I’m trying to understand, like, specifically what you mean.
42 00:06:52.490 ⇒ 00:07:07.589 Advait Nandakumar Menon: But specifically where I want to be, it’s a mix of the technical aspect and business. So, like, really leading a team would be nice, handling the technical aspect would be great, and also.
43 00:07:07.590 ⇒ 00:07:22.959 Advait Nandakumar Menon: handling the business requirements with clients, and getting those requirements, and brainstorming together as a team, and delivering solutions to them is where I see myself going down this path. So, it’s a mix of technical and business aptitude, I would say.
44 00:07:23.360 ⇒ 00:07:24.770 Amber Lin: Gotcha.
45 00:07:24.870 ⇒ 00:07:32.920 Amber Lin: Gosh, okay. So, let me pull up…
46 00:07:37.500 ⇒ 00:07:44.919 Amber Lin: Cool. So, I see here, in your last company, you were working at N Solutions?
47 00:07:46.230 ⇒ 00:07:51.350 Amber Lin: Gotcha, okay. Were you… let’s see…
48 00:07:51.900 ⇒ 00:07:56.079 Amber Lin: Who was your manager when you were at End Solutions?
49 00:07:56.600 ⇒ 00:08:01.730 Advait Nandakumar Menon: So, I had two managers at NSolutions,
50 00:08:01.890 ⇒ 00:08:11.190 Advait Nandakumar Menon: Calvin Nurge was my immediate supervisor, and Jennifer was the other manager at mSolutions. So, I had two managers over there.
51 00:08:12.690 ⇒ 00:08:14.300 Amber Lin: Okay, and…
52 00:08:14.500 ⇒ 00:08:21.620 Amber Lin: how would you say that… how would they rate you out of 10, if I were to ask them?
53 00:08:22.350 ⇒ 00:08:27.020 Advait Nandakumar Menon: I think Calvin would rate me an 8.5 or 9.
54 00:08:27.490 ⇒ 00:08:42.069 Advait Nandakumar Menon: So, he really trusted me with client-facing work and a lot of ownership. So, especially when the requirements were unclear, I was able to deliver things like, sales pipeline dashboards. For example, this was for an insurance client.
55 00:08:42.070 ⇒ 00:08:47.669 Advait Nandakumar Menon: So, by delivering that, I was able to improve the overall forecast accuracy.
56 00:08:47.680 ⇒ 00:08:52.730 Advait Nandakumar Menon: And Jennifer, on the other hand, would give me a similar rating as well, because
57 00:08:52.730 ⇒ 00:09:10.279 Advait Nandakumar Menon: She would probably highlight my reliability and consistency. So, like I mentioned, I was handling multiple client work streams, and she appreciated the fact that I can take something very vague and turn it into something usable without needing constant direction from them, so…
58 00:09:10.420 ⇒ 00:09:14.609 Advait Nandakumar Menon: That… that’s what I think, I would strongly believe they would rate me.
59 00:09:15.330 ⇒ 00:09:22.610 Amber Lin: Okay, sounds good. Are you still at that company, or, are you just around looking for work?
60 00:09:23.190 ⇒ 00:09:40.640 Advait Nandakumar Menon: So I was working on a couple of projects, and all of them wrapped up in December, so my time there ended after that, so I’ve been looking for new roles. So I’ve been very selective about where I want to go into next, and this role, like, Brain Forge as a whole, stood out to me.
61 00:09:40.800 ⇒ 00:09:42.050 Advait Nandakumar Menon: So, yeah.
62 00:09:42.500 ⇒ 00:09:54.140 Amber Lin: I see, cool. If you don’t mind me asking, how come, like, your managers really liked you, and it seemed like you performed really well? Like, how come they didn’t staff you on new projects?
63 00:09:54.590 ⇒ 00:10:09.289 Advait Nandakumar Menon: Yeah, so there was some financial restructuring also happening, so a couple of the contracts weren’t getting renewed for them, so they had to make the unfortunate decision of, letting me go, but it wasn’t…
64 00:10:09.290 ⇒ 00:10:18.179 Advait Nandakumar Menon: because of any performance issues, I feel it’s purely finance… financial, the contracts didn’t add up to them, so… Gotcha.
65 00:10:18.590 ⇒ 00:10:21.029 Amber Lin: Okay, I hear you, that happens all the time.
66 00:10:21.030 ⇒ 00:10:23.330 Advait Nandakumar Menon: Yeah, that… especially nowadays, yeah.
67 00:10:23.330 ⇒ 00:10:34.750 Amber Lin: Yeah, totally understand. When you were at those companies, were you managing a team, or were you mostly, taking assignments and then executing them? What was it like?
68 00:10:35.380 ⇒ 00:10:50.890 Advait Nandakumar Menon: So, this was a very lean team. We were really, like, a team of 3 to 4 people. So, I was not managing a team as per se, but I was really, hands-on with the clients.
69 00:10:51.790 ⇒ 00:11:10.420 Advait Nandakumar Menon: having one-on-one sessions with them, taking the requirements, translating that into their, technical, needs, and a solution, and all those things. So, project management was one of the things I did a little as well in… I mean, in addition to the technical work I was handling, so…
70 00:11:10.420 ⇒ 00:11:13.559 Advait Nandakumar Menon: Taking the requirements and really working on top of it, yeah.
71 00:11:13.920 ⇒ 00:11:27.800 Amber Lin: Gotcha. And in that team of 3 to 4 people, what was the makeup, approximately? Were they all data analysts, or were there some DEs, some people who did it different, or did AE tasks? Like, what was it like?
72 00:11:28.580 ⇒ 00:11:32.310 Advait Nandakumar Menon: Most of us were having a data background.
73 00:11:32.310 ⇒ 00:11:34.970 Amber Lin: There was also one business analyst.
74 00:11:35.110 ⇒ 00:11:49.720 Advait Nandakumar Menon: Calvin and me were really, the one who were working on the data analytics part, taking the requirements and all those things, and there was a junior team member who joined as well, who was a business analyst, so.
75 00:11:49.720 ⇒ 00:11:50.500 Amber Lin: Okay.
76 00:11:50.500 ⇒ 00:12:07.719 Advait Nandakumar Menon: These were some of the people who joined us, but there were some stages wherein we required, like, software developers to work on APIs in order to integrate data from different sources, so in those instances, we usually hired contractors for this kind of work, so not really a part of a team.
77 00:12:07.720 ⇒ 00:12:08.859 Amber Lin: Gotcha, okay.
78 00:12:08.860 ⇒ 00:12:09.580 Advait Nandakumar Menon: Yeah, yeah.
79 00:12:09.580 ⇒ 00:12:17.169 Amber Lin: That makes sense. That’s… I would say that’s pretty similar to what we have. Our teams are also pretty lean, and then we have…
80 00:12:17.590 ⇒ 00:12:31.360 Amber Lin: people from different functions, but we are… because we’re also… mainly also a… we’re a data company, so we also have our in-house DEs and people who help connect that, so I think you’ll be pretty familiar, with that work.
81 00:12:32.000 ⇒ 00:12:44.539 Advait Nandakumar Menon: Yeah, I… the one thing I want to add on top of it is, by working with the different clients, we were collaborating with their DEs and data architects, so that’s something that might be useful for you to know.
82 00:12:44.540 ⇒ 00:12:45.070 Amber Lin: Yeah.
83 00:12:45.070 ⇒ 00:13:07.370 Amber Lin: Totally. I kind of sort of infer that because you said you were working more like a consultant, so I kind of understood how we worked as well. Then following up on that, you said you had Kevin and you had Jennifer. How was the engagements with clients like? So from sale… when sales happened, were they scoping out the projects with clients, or…
84 00:13:07.370 ⇒ 00:13:15.530 Amber Lin: Like, what… how did you get your scope, and how did you move with the requirements? Because you said you also quit communicating with clients.
85 00:13:15.970 ⇒ 00:13:24.679 Advait Nandakumar Menon: Yeah, so, the first, thing with the clients…
86 00:13:24.870 ⇒ 00:13:35.070 Advait Nandakumar Menon: Like, when they are scoping out the work, that… but the next meeting where we are really trying to understand their pain points.
87 00:13:35.120 ⇒ 00:13:41.709 Advait Nandakumar Menon: Their, system, the architecture, and what goes behind the scenes, like, what…
88 00:13:41.750 ⇒ 00:14:00.959 Advait Nandakumar Menon: problem they’re really trying to solve with their, disparate data sources is where I come in to understand all of it, and then have a one-on-one session with Calvin and Jennifer to, see if it makes sense, and it’s something that’s possible to deliver to them. So, that’s how the workflow usually went.
89 00:14:01.530 ⇒ 00:14:05.950 Amber Lin: I see, sorry, the first part, I think our internet wasn’t great, I didn’t.
90 00:14:05.950 ⇒ 00:14:06.540 Advait Nandakumar Menon: Oh, okay.
91 00:14:06.540 ⇒ 00:14:18.269 Amber Lin: catch all of that. So you said, you… do you scope out the work, or do Jennifer and Kevin scope out the work? And then you go… you go look at their systems and architecture?
92 00:14:18.270 ⇒ 00:14:25.940 Advait Nandakumar Menon: Yeah, so the first meeting, the very first meeting with the end clients, Calvin and Jennifer were, scoping out the work.
93 00:14:25.940 ⇒ 00:14:41.730 Advait Nandakumar Menon: And after that, we have a meeting with the client, like, just me and Calvin, and the end client, to understand their pain points, and what they’re trying to answer with their data, and what they really want to arrive at with the end solution. So, that’s where I stepped in. So, yeah.
94 00:14:41.730 ⇒ 00:14:44.470 Amber Lin: I see. Okay, so then…
95 00:14:44.590 ⇒ 00:14:54.319 Amber Lin: And then, do you break down the tasks of what people need to do? Because I know you also have some other team members, or it’s mainly Kelvin that breaks that down?
96 00:14:54.940 ⇒ 00:15:02.930 Advait Nandakumar Menon: It was, I would say, a combination of both of us, because while Galvin would,
97 00:15:03.090 ⇒ 00:15:13.140 Advait Nandakumar Menon: lay out the end delivery or the scope of the project to me, I really had to go and understand what’s possible and what’s not possible, because
98 00:15:13.310 ⇒ 00:15:29.020 Advait Nandakumar Menon: If we are given a deadline of one month, we should be really realistic in setting up expectations, because there are a few things that may not be possible within the one month that we need to scope out and, like, de-scope it, basically, so…
99 00:15:29.090 ⇒ 00:15:40.060 Advait Nandakumar Menon: I would say he would lay out the requirements. I have a real hard look at it and see if it’s possible, and then we adjust accordingly the work pipeline.
100 00:15:40.660 ⇒ 00:15:42.289 Amber Lin: Okay, sounds good.
101 00:15:42.290 ⇒ 00:15:50.659 Advait Nandakumar Menon: We’re halfway through, I want to make sure that you get to ask me some questions, and then if we’re done, I may have some other stuff that I can ask about.
102 00:15:50.660 ⇒ 00:15:51.479 Amber Lin: So, go ahead.
103 00:15:53.080 ⇒ 00:16:00.169 Advait Nandakumar Menon: So, what do you look for in a high-performing candidate?
104 00:16:01.910 ⇒ 00:16:16.780 Amber Lin: So, for your… I think for your position, you’re applying for the Senior Associate Data Insights. So, our team currently, so we’re… we have different departments in our company, so we have the more…
105 00:16:16.780 ⇒ 00:16:24.450 Amber Lin: The AI team that deals with AI clients and internal stuff, we have the data team, that’s more on the…
106 00:16:24.450 ⇒ 00:16:36.319 Amber Lin: technical side, so we have DEs, we have data engineers and analytic… analytics engineers that help with the pipeline and the modeling, and then we have a strategy team, which, you…
107 00:16:36.320 ⇒ 00:16:57.030 Amber Lin: traditionally, you would call them, say, data analysts, business analysts, but it’s more consulting-like of, okay, these are the recommendations we do, and data and dashboards and all that, it just means to end. So that’s kind of the three sections. Depending on the client needs, we pull people from those teams and then staff them on
108 00:16:57.030 ⇒ 00:17:13.319 Amber Lin: say, a 3-5 people team. So some clients are just AI, you don’t need strategy stuff, and some clients, you need strategy, and you will also need, your, say, data engineers to help connect the pipeline. So that’s how teams are.
109 00:17:13.520 ⇒ 00:17:18.189 Amber Lin: So based on that context, I think for your role,
110 00:17:18.859 ⇒ 00:17:29.989 Amber Lin: we are now developing our strategy team, because before, we’re a very, mainly data and then AI company, so right now, because we have the data, we want people who can
111 00:17:30.109 ⇒ 00:17:40.259 Amber Lin: work in more of, like, a consulting style, that helps consult clients and help them find solutions. So I think for that role.
112 00:17:40.669 ⇒ 00:17:46.009 Amber Lin: We’ve had people who had very…
113 00:17:46.149 ⇒ 00:17:56.369 Amber Lin: let’s say, narrow but very deep expertise, and I think we found some trouble of staffing them between the different clients, because once a client
114 00:17:56.399 ⇒ 00:18:10.059 Amber Lin: finishes a project, then maybe their specific need won’t be needed until, say, we find another client later down the road. So, we’re… we’re looking for more of a consulting-style
115 00:18:10.179 ⇒ 00:18:20.709 Amber Lin: person, that has data, data analytics skills that can work with the engineers, and I think that’s why, they were interested in your background.
116 00:18:21.110 ⇒ 00:18:26.539 Advait Nandakumar Menon: Right, right. So, would you say, like, someone who can wear multiple hats
117 00:18:26.610 ⇒ 00:18:41.399 Advait Nandakumar Menon: would be a good fit for this kind of role, because I think that’s what you’re saying, like, someone who can jump between clients and work with different needs, work with analysts and engineers, is that what you’re trying to say?
118 00:18:41.500 ⇒ 00:18:54.060 Amber Lin: Yeah, so, not all… so multifaceted roles, and also being a consultant, very importantly, is being able to communicate and guide the client through things, so there’s gonna be…
119 00:18:54.130 ⇒ 00:19:12.440 Amber Lin: The client’s not always going to be happy, and they’re all sometimes going to be confused, they might not know what they want, so our role is to also tell them, hey, this is… this is… these are the options, this is our recommendation. If you do this, you get that, so that type of, communication skill is very important.
120 00:19:12.440 ⇒ 00:19:25.739 Advait Nandakumar Menon: Yeah, yeah, all of that makes so much sense, and to be honest, that’s what I have done so far in my career as well. Even before n Solutions at Tata Consultancy, like, working as a consultant for other clients, and.
121 00:19:26.140 ⇒ 00:19:30.610 Advait Nandakumar Menon: working towards with the engineers, the architects, it’s something I’ve done
122 00:19:30.840 ⇒ 00:19:38.659 Advait Nandakumar Menon: Throughout my career, so yeah, it’s, I’m really happy to hear about that this is what this role is going to be about, so… Yeah, of course. Yeah.
123 00:19:38.860 ⇒ 00:19:40.430 Advait Nandakumar Menon: Any other questions you have?
124 00:19:41.120 ⇒ 00:19:47.370 Advait Nandakumar Menon: Yeah, I think the next thing I want to ask is, what is your favorite part about working here?
125 00:19:48.340 ⇒ 00:19:54.449 Amber Lin: Okay. I’ve worked here for about a year now, and…
126 00:19:54.600 ⇒ 00:19:59.950 Amber Lin: I think a few things that I liked, one is the…
127 00:20:00.150 ⇒ 00:20:11.680 Amber Lin: I would say the working style, so that includes the hours that I work, or, say, the flexibility of working from home, and being able to
128 00:20:12.110 ⇒ 00:20:15.149 Amber Lin: Choose my working hours some way.
129 00:20:15.150 ⇒ 00:20:15.540 Advait Nandakumar Menon: Right.
130 00:20:15.540 ⇒ 00:20:32.879 Amber Lin: So our requirements is that… that we have overlaps with, I think, the ESC time zone for 4 hours, and then be able to attend the meetings, and then we work internally to say, okay, it’s too late for you, let’s move this meeting, but to be able to attend most of the meetings.
131 00:20:32.880 ⇒ 00:20:36.209 Amber Lin: And then, other than that, then I will be able to say.
132 00:20:36.210 ⇒ 00:20:51.960 Amber Lin: do my analysis early in the morning or later at night, so as long as I have a deliverable for the day, and I’m able to say I stand up tomorrow, like, hey, I did this, this is what I found, nobody’s really forcing you to work a specific 9 to 5.
133 00:20:51.980 ⇒ 00:20:56.059 Amber Lin: And I think combined with working from home, that’s a really big advantage.
134 00:20:56.150 ⇒ 00:21:00.480 Amber Lin: So that’s the flexibility part. I think the second part is
135 00:21:00.750 ⇒ 00:21:07.100 Amber Lin: I really like the people that work here, because… I would say…
136 00:21:08.090 ⇒ 00:21:13.720 Amber Lin: I think that’s the more important part, other than, like, tolerating.
137 00:21:13.720 ⇒ 00:21:14.230 Advait Nandakumar Menon: Of course.
138 00:21:14.230 ⇒ 00:21:19.070 Amber Lin: Hours, or, like, having reasonable compensation, and having people that you like.
139 00:21:19.510 ⇒ 00:21:19.860 Advait Nandakumar Menon: It’s fair.
140 00:21:19.860 ⇒ 00:21:25.450 Amber Lin: important. I think they’re all kind people, which is a, like, a…
141 00:21:26.090 ⇒ 00:21:40.420 Amber Lin: harder thing to say, to say, like, everybody on the team is… is kind-hearted, and then they… they’re also very smart, so, like, working with them is very… it’s very nice. They all collaborate, they don’t, like,
142 00:21:40.550 ⇒ 00:21:49.309 Amber Lin: they won’t purposely say, I don’t want to do this, this is not my problem, like, you have to ask someone else. So, working with them is nice, and then I think lastly.
143 00:21:49.720 ⇒ 00:21:58.529 Amber Lin: On my own career, on my own learnings, being interested in the work was something that was very important to me, so…
144 00:21:58.530 ⇒ 00:22:14.890 Amber Lin: I used to be a consultant, and then I… when I started at the company, I started as a more technical project manager, and I kind of went back to doing data and doing consulting work. So, having… being able to move between things and being able to…
145 00:22:15.290 ⇒ 00:22:15.720 Advait Nandakumar Menon: Yeah.
146 00:22:15.720 ⇒ 00:22:26.220 Amber Lin: Yeah, pick up the AI tools that we developed internally to make work easier, to pick up, say, AI tools that I use currently format analysis.
147 00:22:26.220 ⇒ 00:22:41.609 Amber Lin: So, like, having… seeing that and seeing how it compares to, before, when I worked at a big four, it was very different, so I do appreciate this… this style and this working at… over here at this company.
148 00:22:42.200 ⇒ 00:23:00.409 Advait Nandakumar Menon: Right. No, all of this sounds, like, really, appealing to me, like, the flexibility, the, love, or… not love exactly, but the teamwork that you are doing together, but yeah, this sounds, very intriguing to me, so thanks for that insight.
149 00:23:00.670 ⇒ 00:23:05.550 Amber Lin: Yeah, of course. Anything else you are interested in?
150 00:23:06.350 ⇒ 00:23:08.890 Advait Nandakumar Menon: So…
151 00:23:09.380 ⇒ 00:23:20.339 Advait Nandakumar Menon: I think this will be my last question I have, but based on what you have learned about me so far, do you have any doubts that I could succeed in a role like this?
152 00:23:21.770 ⇒ 00:23:23.780 Amber Lin: Let’s see, so…
153 00:23:24.580 ⇒ 00:23:35.899 Amber Lin: our interview pipeline, I think operations just send you how it works, how our interview works, so I’m the first interviewer, and if they decide that you would proceed, then there’s a…
154 00:23:35.950 ⇒ 00:23:46.099 Amber Lin: next round technical interviews, and after that, that’s a… I think there’s a technical assessment, and then the panel interview. So 3 interviews total.
155 00:23:46.100 ⇒ 00:23:57.129 Amber Lin: Right. So, right now, because I’m not asking you about specific technical things, I wouldn’t be able to say if this person is a fit.
156 00:23:57.260 ⇒ 00:24:01.070 Amber Lin: Technically, but, like, so far.
157 00:24:01.430 ⇒ 00:24:15.279 Amber Lin: I don’t think I have specific doubts on, say, how you communicate, how you carry yourself, or how you would fit in the team. So I don’t think I will be able to tell you that.
158 00:24:15.280 ⇒ 00:24:29.729 Advait Nandakumar Menon: No, no, yeah, yeah, that’s fine. I was just, trying to, if any concern you had, you mentioned I was trying to, correct or give you better insights about it, so… yeah, no, that’s totally fine. I was just trying to understand that.
159 00:24:29.980 ⇒ 00:24:31.030 Amber Lin: Yeah, totally.
160 00:24:31.080 ⇒ 00:24:50.889 Amber Lin: Okay, I… I don’t really have too many questions at this point. I really enjoyed the time talking to you, and thank you for taking the time talking. I’ll send the… I’ll send my notes to the team, and I believe the operations team will get back to you with the next steps or results.
161 00:24:51.460 ⇒ 00:24:52.680 Advait Nandakumar Menon: Yep, sounds good.
162 00:24:52.920 ⇒ 00:24:57.180 Amber Lin: Alright, thank you so much. It was nice talking to you. Yeah. Have a nice one. Bye.
163 00:24:57.640 ⇒ 00:24:58.230 Advait Nandakumar Menon: Right.