Meeting Title: Brainforge Interview w- Greg Date: 2026-02-18 Meeting participants: Deepika Sethi, Greg Stoutenburg
WEBVTT
1 00:11:08.250 ⇒ 00:11:09.290 Greg Stoutenburg: Hello!
2 00:11:10.030 ⇒ 00:11:11.659 Deepika Sethi: Hey, hi Craig, how are you?
3 00:11:11.660 ⇒ 00:11:13.920 Greg Stoutenburg: Hey, great, how are you?
4 00:11:13.920 ⇒ 00:11:14.809 Deepika Sethi: I’m good too.
5 00:11:15.120 ⇒ 00:11:20.119 Greg Stoutenburg: Hey, thanks for your patience. Tell me, is it, Deepika?
6 00:11:20.470 ⇒ 00:11:21.460 Deepika Sethi: Yeah, it’s…
7 00:11:21.630 ⇒ 00:11:28.789 Greg Stoutenburg: Yeah, Greg, nice to meet you. Yeah, well, you know, glad that we have this opportunity to talk today.
8 00:11:29.420 ⇒ 00:11:30.510 Deepika Sethi: Likewise.
9 00:11:30.780 ⇒ 00:11:43.549 Greg Stoutenburg: Yeah, just… just to help me clarify, so I’m, this is the… this… this is your second interview, right, with Brainforge? Okay, yeah, cool. So, yeah, I’m Greg, I’m a digital product analyst here at Brainforge.
10 00:11:43.550 ⇒ 00:12:00.179 Greg Stoutenburg: And so my, my role for the interview is… this is sort of, like, the role-focused interview, like, the one that sort of focused on, like, the meat of the job. So we’ll ask some questions around, like, you know, how you’d handle different situations and things like that, and about your background, so…
11 00:12:00.180 ⇒ 00:12:04.549 Greg Stoutenburg: Yeah, just, just so I can clarify, just kind of looking at my notes here,
12 00:12:04.960 ⇒ 00:12:18.859 Greg Stoutenburg: You’ve applied for the, Senior Associate for Data Insights position, is that right? Okay, alright, great. I just want to make sure I’m looking at the… Ask the right questions. Not that I’m just, like, looking at a list of questions on the other screen.
13 00:12:19.760 ⇒ 00:12:32.910 Greg Stoutenburg: That is what I’m doing. Okay, cool. So, yeah, I mean, maybe let’s just take, like, you know, 3 minutes or so, and tell me, you know, a little bit about yourself and how you ended up here, talking to me at Brainforge.
14 00:12:33.460 ⇒ 00:12:35.660 Deepika Sethi: Sure, so,
15 00:12:35.660 ⇒ 00:12:45.000 Deepika Sethi: I have around a decade of industry experience. I’ll start with the relevant ones. Currently, I’m pursuing an MS in Information Technology from Gabili School.
16 00:12:45.000 ⇒ 00:13:04.040 Deepika Sethi: Prior to this, I was working with London Stock Exchange Group as a product owner for Operation Resilience. So my work there was… we were majorly focused on operational resilience reporting, where users will report the data around IT services and the applications downtime, and making sure that operations are resilient.
17 00:13:04.040 ⇒ 00:13:15.410 Deepika Sethi: So, I was working towards the back end, where we were trying to automate these things, and we started with the… I was working on two parts. One was data governance, where we had started identifying the processes that
18 00:13:15.410 ⇒ 00:13:25.600 Deepika Sethi: That are there for the team, and trying to identify the critical data elements and their authorized data source, and ensure that entire pipeline is created from source.
19 00:13:25.640 ⇒ 00:13:43.119 Deepika Sethi: to centralized database to ultimately reporting. The reporting there was based on Power BI. Second thing was another initiative that we were trying to do. It was not yet fully done. We were in… we were trying to see if we can use AI to automatically fetch the regulations and map it to the
20 00:13:43.120 ⇒ 00:13:49.679 Deepika Sethi: company’s internal policies and controls, so as to ensure that we are fully compliant. We were still in,
21 00:13:49.680 ⇒ 00:13:57.390 Deepika Sethi: A stage where we were… we had a problem statement defined, but we were looking at the product vision and the practices to be used.
22 00:13:57.650 ⇒ 00:14:08.080 Deepika Sethi: And the solutioning was yet not started. Prior to that, I have got, quite some experience with regulatory reporting and banking, with Dodge Bank and Credit Suisse.
23 00:14:08.080 ⇒ 00:14:20.809 Deepika Sethi: In every jurisdiction, you know, every bank kind of has to furnish some reports to the centralized… central bank, and I was working on the automation of those reports for the entire end-to-end pipeline, fetching the data.
24 00:14:20.810 ⇒ 00:14:40.339 Deepika Sethi: getting it into the centralized database, doing, technical and business validations, and then ultimately populating the report-specific logic and generating the report using a tool called Axiom. So, this is, like, last two, relevant experiences that I have. Prior to that, I have worked as, like, automation,
25 00:14:40.340 ⇒ 00:14:46.780 Deepika Sethi: engineer, and probably a little bit in, in change management in ESG domain.
26 00:14:47.670 ⇒ 00:14:55.050 Greg Stoutenburg: Okay, okay, great. Cool. Alright, that’s, that’s very helpful. So, a lot, sort of, in the finance sector.
27 00:14:55.730 ⇒ 00:14:56.589 Deepika Sethi: Yeah, me too.
28 00:14:56.590 ⇒ 00:15:07.360 Greg Stoutenburg: Yeah. Yeah, yeah, yeah, cool. Okay, yeah, that’s really helpful. So, yeah, let’s, let’s, let’s jump into some of the specifics here, then.
29 00:15:07.580 ⇒ 00:15:12.289 Greg Stoutenburg: So… How would you turn a…
30 00:15:12.560 ⇒ 00:15:29.910 Greg Stoutenburg: vague question into an analysis plan. So, some stakeholder comes to you, they’ve got some kind of ask, they’ve got some kind of need, but they haven’t really presented it well. And you need to turn that into some… some project that’s going to lead to an analysis and some takeaways. How do you get started on something like that?
31 00:15:30.150 ⇒ 00:15:35.149 Deepika Sethi: So, again, everything, I think, starts with someone coming with some random questions.
32 00:15:35.150 ⇒ 00:15:55.299 Deepika Sethi: My approach is, you know, first to have that first meeting, trying to gather as much as information about what they are looking at, and at times it happens they had not really envisioned something. Then I try to go back, do my homework, come up with what I have understood, do my research, what can I really make out of it? Let’s say user says I need an app to
33 00:15:57.030 ⇒ 00:16:14.459 Deepika Sethi: app to, like, store information. Now, it’s very vague, right? So I’ll go and try to understand what domain are we in, what kind of information do we have, structured, unstructured, where can I store it, and how can I store it? I’ll just create not really a very detailed prototype, but something around what I understood.
34 00:16:14.460 ⇒ 00:16:17.489 Deepika Sethi: Go back to user with exact questions, right?
35 00:16:17.570 ⇒ 00:16:36.280 Deepika Sethi: See, this is what my understanding is. First, whether it is correct. If it is, I have these many questions. I’ll try to have that work done and go back to users to understand that what I’m doing is right. Maybe some workflows, process workflows, or user journey diagrams, if I have enough information around things.
36 00:16:36.870 ⇒ 00:16:51.790 Greg Stoutenburg: Yeah, yeah, good. Now, when you go back to the user, or to the, you know, to the team, the requester, and you say, yeah, you know, this is what you asked for, and you give them their, you know, sort of vague language, and here’s… here are these different paths we could take.
37 00:16:52.030 ⇒ 00:17:02.469 Greg Stoutenburg: have you ever had a time, or can you… or can you describe what you would do in a time, where they’re like, no, it’s none of those? What I said the first time is what I want you to do.
38 00:17:03.600 ⇒ 00:17:19.060 Deepika Sethi: Okay, so, again, I would then… since I jumped a step ahead in the previous one, I would like to reframe the problem statement as precise as I can, so as to make sure that the work doesn’t really go.
39 00:17:19.099 ⇒ 00:17:25.229 Deepika Sethi: Probably, then, rather than asking questions on the solutioning, I would try to ask questions on the…
40 00:17:25.560 ⇒ 00:17:44.379 Deepika Sethi: vision and the problems type of it. So let’s say if they say, no, this is not, then I’ll start with the process, right? What do they do? What are their pain points? Why do they really need the solution? Maybe that might help me to understand the perspective they are coming from, and better define the problem statement for me.
41 00:17:45.030 ⇒ 00:18:04.579 Greg Stoutenburg: Yeah, yeah, yeah. Yeah, yeah, that’s good. I mean, and, right, so many… so many questions that look like they’re insights questions or requests for analysis are really, like, requests for clarity, right? The user’s trying to… there’s something that they don’t understand, and they need your help understanding it, and they might not realize how extensive their lack of
42 00:18:05.030 ⇒ 00:18:09.960 Greg Stoutenburg: insight or understanding is, right, until you help them. Yeah, so that’s… that’s really good.
43 00:18:10.190 ⇒ 00:18:15.489 Greg Stoutenburg: Okay, great. What… what, in your opinion makes an analysis actionable?
44 00:18:16.930 ⇒ 00:18:24.160 Deepika Sethi: Okay, First thing is, again, clarity, then…
45 00:18:24.420 ⇒ 00:18:36.430 Deepika Sethi: probably, if I have to work on it, I need clear details of the logic. It can be either in terms of action, in terms of exec logic, or maybe a process flow, some sort of…
46 00:18:37.010 ⇒ 00:18:38.450 Deepika Sethi: I would say off…
47 00:18:38.880 ⇒ 00:18:51.660 Deepika Sethi: workflow which helps me understand what needs to be done. And third thing would be metrics, because if I do an action without knowing what my measures are, then there’s no point, right? I can’t really judge whether I’m doing something right or wrong.
48 00:18:51.890 ⇒ 00:18:54.119 Deepika Sethi: So that would be my three things, though.
49 00:18:54.120 ⇒ 00:19:00.219 Greg Stoutenburg: So it has to be… okay, so there have to be metrics tied to it, and there has to be, like, a plan of action.
50 00:19:00.220 ⇒ 00:19:01.860 Deepika Sethi: Some workflow and plans out.
51 00:19:01.860 ⇒ 00:19:12.099 Greg Stoutenburg: Yeah, yeah. Well, okay, good. What would make… what would make an analysis, not actionable? Or, like, can you describe a situation where,
52 00:19:12.260 ⇒ 00:19:17.359 Greg Stoutenburg: Where someone wanted some reporting on something, and you thought it wasn’t as valuable as it could be.
53 00:19:18.810 ⇒ 00:19:20.480 Deepika Sethi: Let me think about it.
54 00:19:22.110 ⇒ 00:19:28.420 Deepika Sethi: Something not actually. I have worked in industries where I, most of the times, knew what needs to be done, let me think.
55 00:19:28.420 ⇒ 00:19:28.970 Greg Stoutenburg: stop.
56 00:19:30.750 ⇒ 00:19:50.520 Deepika Sethi: I think probably, again, it will start with when you do not really have a clarity around that, and secondly, there are times when we jump into solutioning really early without understanding the feasibility of doing a feasibility check, or maybe doing a quick prototype to understand what exactly is needed.
57 00:19:50.520 ⇒ 00:19:51.890 Deepika Sethi: So, when you do not have
58 00:19:52.480 ⇒ 00:20:00.819 Deepika Sethi: More than clarity, if you have not even envisioned what you’re trying to create, you know, any sort of product vision you need, right? Maybe some…
59 00:20:01.020 ⇒ 00:20:08.679 Deepika Sethi: You could say a view in your mind about how the screen would look. If you haven’t really done that, that will not really done up very well.
60 00:20:09.370 ⇒ 00:20:11.519 Greg Stoutenburg: Yeah, okay, okay, yeah.
61 00:20:11.820 ⇒ 00:20:21.740 Greg Stoutenburg: Yeah, I can think of times in, in my career where I’ve been on the, you know, sort of had reporting responsibilities for stakeholders who,
62 00:20:21.740 ⇒ 00:20:41.910 Greg Stoutenburg: you know, they felt the pressure from their boss or from executives to just, like, show lots of data in monthly charts or whatever. And so they just wanted to slice and dice every single data point, but it wasn’t actually, you know, it didn’t lead… it didn’t fit into any kind of plan. That was always a challenge. I said, we want to help, right? We data people, we want to help.
63 00:20:41.910 ⇒ 00:20:48.960 Greg Stoutenburg: But what you’re asking us to do is not helping. Let us help you, yeah. Okay, great.
64 00:20:49.320 ⇒ 00:21:04.790 Greg Stoutenburg: As far as, like, as far as, like, the way you work on, delivery, like, tell me about a time when you avoided over-engineering. Like, maybe there was, like, a really complicated way to solve some problem, and you chose a simpler way that was faster. Can you give an example like that?
65 00:21:05.600 ⇒ 00:21:11.810 Deepika Sethi: Yeah, I think, while I was working with Dodge Bank, there was a particular report around,
66 00:21:11.880 ⇒ 00:21:27.700 Deepika Sethi: Thailand reports where they were, you know, trying to… instead of really getting the outside report, they were trying to standardize the data. It itself is called data transformation reports. So there were a set of reports where, you know, it was expected that
67 00:21:27.860 ⇒ 00:21:44.769 Deepika Sethi: input of one report will go into another, and then that report will work it out, and we had a hard time really working it out, how do we do? So we, again, started with the very basics of the requirement, trying to feed in information from one to another, and it kind of created a very complicated loop.
68 00:21:44.860 ⇒ 00:22:00.660 Deepika Sethi: So we took a step back. I didn’t work alone, I worked with my team. We sat down and tried to, you know, figure out how we could really simplify it. So what we realized was, though, the reports were interdependent, they were basically consuming the same data elements.
69 00:22:00.920 ⇒ 00:22:06.019 Deepika Sethi: at this 80% same data elements, they were just reading those in a very different view.
70 00:22:06.020 ⇒ 00:22:23.579 Deepika Sethi: So when we came out, we realized that we could actually solve the problem by consolidating those data points in one point. Instead of basically creating dependencies on the output, we created dependencies on the input, which simplified how we really processed the data, because we had now the standardized data instead of
71 00:22:23.710 ⇒ 00:22:25.710 Deepika Sethi: Really working it out.
72 00:22:25.930 ⇒ 00:22:31.929 Deepika Sethi: kind of re-engineering the data again from the output and then using it. So that’s how… at once.
73 00:22:31.930 ⇒ 00:22:36.939 Greg Stoutenburg: Yeah, that’s great. And now, as far as working on the team there,
74 00:22:37.120 ⇒ 00:22:42.539 Greg Stoutenburg: Were you the data engineer, or did you work with a data engineer who did that in the background?
75 00:22:42.900 ⇒ 00:23:05.549 Deepika Sethi: the structure of our team was more like… I was a product owner or business analyst, which was looking both from the data ingestion side of it and the reporting, information side of it. Then I had technical members from my team, developers from the reporting side, and a little bit of members who worked on the data pipeline. So we all worked together. So my work wasn’t really to…
76 00:23:05.560 ⇒ 00:23:15.310 Deepika Sethi: Explicitly as doing the entire data engineering, but it was more understanding the logic, understanding how the data flows, and then work with the team to really put a… implement it that.
77 00:23:15.490 ⇒ 00:23:28.849 Greg Stoutenburg: Yeah, yep, yeah, perfect. Yeah, perfect, I know exactly what you’re talking about. So, so you were defining… you were in the role of defining the requirements and the expected flow, and then, and then the engineer did the, the back-end work. Yeah, great.
78 00:23:28.850 ⇒ 00:23:37.000 Greg Stoutenburg: Okay, here’s another one. When do you choose spreadsheets over a business intelligence tool? When is it easier to just stick to a spreadsheet?
79 00:23:39.010 ⇒ 00:23:59.899 Deepika Sethi: when there’s less of visualization. I’m personally a pretty much fan of Excels, but yeah, I understand those are really not a good tool for data visualization. So when I need more of a tabular data, or something where I need to do a quick calculation, I prefer spreadsheets, but when it’s more around charts and presenting
80 00:23:59.960 ⇒ 00:24:05.210 Deepika Sethi: Summarized information, or ultimately, you know.
81 00:24:05.340 ⇒ 00:24:08.470 Deepika Sethi: the output information, I prefer a visualization tool.
82 00:24:08.770 ⇒ 00:24:18.099 Greg Stoutenburg: Yeah, yeah, yeah, yeah, okay, yeah, great. So, so things that are repeatable, you want to keep in the BI tool, and, things that, you know, a calculation.
83 00:24:18.100 ⇒ 00:24:26.489 Deepika Sethi: Yeah, where I have a kind of output with some calculation, it goes into the BI tool, but if I have to do some, sort of a…
84 00:24:26.600 ⇒ 00:24:30.710 Deepika Sethi: calculations or logic on any information, I prefer Excel or…
85 00:24:30.710 ⇒ 00:24:50.660 Greg Stoutenburg: Yeah, got it. Great, great. What’s your approach to getting some quick wins in the first 30 days? So, whether that’s, you know, like, at a new employer for a client, what’s something that you do to, you know, sort of demonstrate some value, show, you know, here’s the direction that we’re going in, you’re in good hands. How do you show that sort of thing in the first month?
86 00:24:51.790 ⇒ 00:25:01.189 Deepika Sethi: Okay, so, there I’ve… we have… even in the industry, have been following that MVP approach, where we try two things. One is to try to create a prototype quickly.
87 00:25:01.190 ⇒ 00:25:20.559 Deepika Sethi: probably a week or so, so we understand what we are going to build as really adds value. Second will be to then move to either a pilot or MVP. Lately, we are moving away from pilots, reason being those are really not scalable. Then we start looking at MVPs with the very minimal information. So let’s say I have to process a payment.
88 00:25:20.560 ⇒ 00:25:28.049 Deepika Sethi: I would try to have the payment thing, probably I might not be able to connect to the outside, but I’ll use the mock data, but it would be something
89 00:25:28.420 ⇒ 00:25:46.279 Deepika Sethi: quite similar to what an actual product will be, so that it’s just maybe replacing the data source or replacing one API, rest everything remains same for my… So we’ll try to have that very basic functionality, which we can do quick. Again, when I have to decide the functionality, it should have three things, like impact.
90 00:25:46.430 ⇒ 00:25:58.629 Deepika Sethi: there’s no point of showing the functionality which doesn’t really… which nobody wants. So whether it’s really how much impact is it, the value it creates, then the effort and the timelines around it. As you mentioned, if it’s 30 days, I have to take a…
91 00:25:58.630 ⇒ 00:26:09.110 Deepika Sethi: Something which I can really do it in 30 days, and it really helps the user. And then it’s the complexity. If it’s very complex, I do not really have the team who can implement it
92 00:26:09.270 ⇒ 00:26:16.460 Deepika Sethi: No point starting that element. So, these three… after keeping these three things in mind, we try to take the…
93 00:26:16.770 ⇒ 00:26:22.740 Deepika Sethi: Simplest chunk of the problem, which delivers some value, and try to create it in a way it is scalable.
94 00:26:23.430 ⇒ 00:26:26.249 Greg Stoutenburg: Yeah, yeah, very good. So basically, find the simplest piece.
95 00:26:26.250 ⇒ 00:26:26.940 Deepika Sethi: Yeah.
96 00:26:26.940 ⇒ 00:26:32.300 Greg Stoutenburg: Is that what I’m hearing? Yeah, yeah, great, yeah. Yeah. Alright.
97 00:26:37.630 ⇒ 00:26:52.560 Greg Stoutenburg: Okay, here’s one. How do you ensure clients can operate your work without you? So say you’ve, you know, you’ve done some analysis, you’ve set something up in a BI tool. How do you ensure that clients can carry on and look at your work without you having to be involved directly every time?
98 00:26:53.460 ⇒ 00:26:54.110 Deepika Sethi: Okay.
99 00:26:54.160 ⇒ 00:27:11.200 Deepika Sethi: So, again, I think one thing will be documentation that comes in that you should have. I’m not really saying the extensive documentation, but documentation… documentation enough to let someone understand what you’re really trying to build. Second will be the way, I think, a lot more talk about UI and UX.
100 00:27:11.200 ⇒ 00:27:20.339 Deepika Sethi: User, I believe the easier your product is to use, the ease of, usability makes sense. Ideally, the product should be very intuitive if you are in the same domain.
101 00:27:20.650 ⇒ 00:27:25.500 Deepika Sethi: the requirement has come from the user, right? If they have to really Aww.
102 00:27:25.540 ⇒ 00:27:45.230 Deepika Sethi: get confused with using the product, it doesn’t really give them any value. Third will be to keep involving the users across the lifeline, so that they already know what’s happening, and they don’t really need me every time, whenever there’s an update, or whenever there’s a new feature being rolled out, or whenever there’s an update in the product.
103 00:27:45.800 ⇒ 00:27:50.159 Greg Stoutenburg: So the more you’re working together already, the easier it’s going to be.
104 00:27:50.330 ⇒ 00:27:51.559 Greg Stoutenburg: Is part of it.
105 00:27:52.570 ⇒ 00:27:53.520 Greg Stoutenburg: Yeah.
106 00:27:54.010 ⇒ 00:27:59.050 Greg Stoutenburg: Yeah, yeah, yeah, you can push back. If that summary wasn’t accurate, please correct me.
107 00:27:59.330 ⇒ 00:28:09.539 Deepika Sethi: Oh, yeah, I think it makes, I just, started with the solutioning part and not with the working tool. Yeah, collaboration makes sense. The more you collaborate, it’s easier to
108 00:28:09.720 ⇒ 00:28:13.029 Deepika Sethi: get things, done without you. The team is independent enough.
109 00:28:13.350 ⇒ 00:28:18.789 Greg Stoutenburg: Yeah, yeah. Yeah, well, just to… just to sort of, like, go a little further on that one,
110 00:28:19.340 ⇒ 00:28:36.200 Greg Stoutenburg: Suppose you’ve been working with your client, or working with the external team, and collaborating a lot, but, you know, you want your… you want your work to be self-serviceable by them, sort of, as much as possible, right? So they don’t have to lean on you for every… for every request.
111 00:28:36.580 ⇒ 00:28:45.300 Greg Stoutenburg: How do you ensure that as they continue to onboard new members to their team, that they’re able to rely on your work and get the insights they’re looking for?
112 00:28:46.040 ⇒ 00:28:54.379 Deepika Sethi: Okay, so then I think, again, I’m expecting if it’s new member, they might not have really got a chan- gotten a chance to collaborate already.
113 00:28:54.490 ⇒ 00:29:00.120 Deepika Sethi: I think documentation does… and the ease of use does come handy in that case.
114 00:29:00.400 ⇒ 00:29:01.060 Greg Stoutenburg: Yeah.
115 00:29:01.520 ⇒ 00:29:03.460 Greg Stoutenburg: Yeah, just good documentation.
116 00:29:03.460 ⇒ 00:29:04.040 Deepika Sethi: Yeah.
117 00:29:04.040 ⇒ 00:29:05.430 Greg Stoutenburg: Yeah, yeah, yeah, okay.
118 00:29:05.590 ⇒ 00:29:08.940 Greg Stoutenburg: Cool, okay,
119 00:29:09.110 ⇒ 00:29:18.680 Greg Stoutenburg: Describe experiences you’ve had explaining technical findings to a non-technical executive. Specifically an executive, not just a stakeholder, but, you know, someone in leadership.
120 00:29:19.260 ⇒ 00:29:20.040 Deepika Sethi: Okay.
121 00:29:20.480 ⇒ 00:29:24.020 Deepika Sethi: So shall I, like, take an example or something?
122 00:29:24.340 ⇒ 00:29:25.930 Greg Stoutenburg: Yeah, an example would be great.
123 00:29:26.500 ⇒ 00:29:31.210 Deepika Sethi: Okay, so, I was majorly in reporting and,
124 00:29:31.750 ⇒ 00:29:39.259 Deepika Sethi: the rep… it’s the report that these guys understand. They don’t really understand where the data came from, where the things came from.
125 00:29:39.340 ⇒ 00:29:56.990 Deepika Sethi: So, I try to really dig down on the kind of data that they need, and the business value of that report. So, let’s say I was working on OTC reports, or they understand the product as forward, or understand the product as swap, but they really don’t understand how did I put a filter on it.
126 00:29:57.070 ⇒ 00:30:09.750 Deepika Sethi: So rather than telling that, okay, this is the attribute where I put a filter to get the report, I’ll tell them, this report only reports, or only has the data for these products, for what something and something.
127 00:30:09.750 ⇒ 00:30:19.580 Deepika Sethi: And when it comes to the legs of the swap, a swap has two legs, I don’t know if you have worked in finances, but it has two legs, which is a business word, but
128 00:30:19.600 ⇒ 00:30:23.070 Deepika Sethi: Towards the technical, we would have data elements, right?
129 00:30:24.560 ⇒ 00:30:30.499 Deepika Sethi: something more like, leg 1, leg 2, instead of pay or receive leg. We will have,
130 00:30:30.660 ⇒ 00:30:48.469 Deepika Sethi: strike price as just the price. So instead of going for those generic words, I will try to find out the specific words corresponding to the report, and try to use that, domain-specific language, which the business understands, and try to make sure that whatever filters we are putting in.
131 00:30:48.490 ⇒ 00:30:58.939 Deepika Sethi: This is in terms of the upstream system, which they have seen, or maybe, again, if that’s possible, to go into domain terms, use that instead of really using the technical jargons around.
132 00:31:00.040 ⇒ 00:31:06.399 Greg Stoutenburg: Yeah, yeah, so in general, like, a preference for less technical jargon when discussing things with the executives.
133 00:31:06.670 ⇒ 00:31:10.799 Deepika Sethi: Yeah, maybe a little more of business or domain jargons, if I can use some.
134 00:31:10.800 ⇒ 00:31:12.100 Greg Stoutenburg: Yeah. Yeah.
135 00:31:12.250 ⇒ 00:31:15.359 Greg Stoutenburg: Yeah, good.
136 00:31:15.650 ⇒ 00:31:20.600 Greg Stoutenburg: How do you handle a client disagreeing with your logic that you’ve used to try to solve a problem?
137 00:31:21.610 ⇒ 00:31:24.720 Deepika Sethi: In that case, I would,
138 00:31:25.000 ⇒ 00:31:29.949 Deepika Sethi: Again, firstly, everything starts with a communication. Firstly, I’ll try to understand if
139 00:31:30.070 ⇒ 00:31:42.890 Deepika Sethi: there’s something that has been missing, or what really they think is wrong. If, let’s say, they can put it in words, well and good. If not, I would try to take the data with me. So let’s say, as you mentioned, that they disagree with the logic.
140 00:31:42.980 ⇒ 00:31:57.379 Deepika Sethi: I know they don’t understand the logic in terms of technicalities, but I do understand where the data came from, what was the data, and what did we exactly do in business terms. So, let’s say if I have converted an amount from, let’s say, USD to Euro, and that’s what
141 00:31:57.460 ⇒ 00:32:15.710 Deepika Sethi: is creating a difference. I don’t need to tell them this is where I got the data, and I did a multiplication or something, I just need to tell them, okay, the data was converted. So I will try to take that data, the logic with them, and then try to understand, kind of backtracing what really went wrong, and then try to solve it out.
142 00:32:15.920 ⇒ 00:32:24.530 Deepika Sethi: This time, it will be more of a reverse engineering approach, going… drilling down, but where exactly did we go wrong, tend to…
143 00:32:25.020 ⇒ 00:32:29.200 Deepika Sethi: Just looking at the wards. More data-driven approach.
144 00:32:29.200 ⇒ 00:32:33.360 Greg Stoutenburg: Yeah, okay, alright, so basically work back to trying to figure out what the source of the disagreement is.
145 00:32:33.640 ⇒ 00:32:34.260 Deepika Sethi: Yeah.
146 00:32:34.450 ⇒ 00:32:37.119 Greg Stoutenburg: Yeah, yeah, okay, great, yeah,
147 00:32:37.540 ⇒ 00:32:46.980 Greg Stoutenburg: Okay, 1.23. I should choose maybe one more. Tell me about,
148 00:32:48.320 ⇒ 00:32:58.670 Greg Stoutenburg: Tell me about a template, or a system, or some kind of automation that you improved. So something that’s, like, repeatable, that you have improved for future use.
149 00:32:59.560 ⇒ 00:33:08.610 Deepika Sethi: Okay, I was working in automation, so I’ll probably try to get something which was not really a requirement, because most of the requirements worked.
150 00:33:09.190 ⇒ 00:33:12.360 Deepika Sethi: Let me think about it, just a few minutes.
151 00:33:12.360 ⇒ 00:33:14.039 Greg Stoutenburg: Yeah. Yeah.
152 00:33:14.760 ⇒ 00:33:24.909 Deepika Sethi: Yeah, so, this was, again, while I was working with London Stock Exchange Group, we could notice that, there were no data descriptions with most of the data elements.
153 00:33:25.020 ⇒ 00:33:35.989 Deepika Sethi: And these were not probably available in a single source. We did have multiple sources, and there were defined sources from where we could really get the data. And everyone was
154 00:33:35.990 ⇒ 00:33:54.650 Deepika Sethi: whoever was working on data was doing the same thing, going through multiple sources and trying to fetch the information. I know this is really not the best way, but at least for the time being, this was the best approach, that instead of every… we weren’t really getting on the single definition, but we were trying to find out something based on the sources where we created
155 00:33:54.650 ⇒ 00:33:58.369 Deepika Sethi: a simple lookup kind of thing in Python, where it would go.
156 00:33:58.370 ⇒ 00:34:10.120 Deepika Sethi: Using the regex search, the word that you’re looking for, and fetch the information out there, so that at least we have… and if the data had, like, 5
157 00:34:10.159 ⇒ 00:34:17.519 Deepika Sethi: Definitions, we were still fetching 5, so that one can really use what they want, but we were trying to fetch that information instead of doing manually.
158 00:34:18.540 ⇒ 00:34:26.300 Greg Stoutenburg: Yeah, okay, fantastic, yeah. Alright, so that’s all that I have.
159 00:34:26.429 ⇒ 00:34:32.749 Greg Stoutenburg: You know, you’re invited. If you have any questions for me, you can ask, but that’s it from my end for now.
160 00:34:32.750 ⇒ 00:34:38.730 Deepika Sethi: I have a few, sorry. One thing being, I understand that Brainforges,
161 00:34:39.420 ⇒ 00:34:50.039 Deepika Sethi: remote, works remotely. So, how does, really, the growth look like in terms of… in terms of, you know, promotions or roles? How does that work in that case?
162 00:34:50.820 ⇒ 00:35:04.299 Greg Stoutenburg: Yeah, I think that’s something that, the operations person who reached out can help with more. I’m, yeah, I’m just, I would be, a peer, at the organization rather than, someone on the ops team.
163 00:35:04.450 ⇒ 00:35:06.610 Greg Stoutenburg: So, sorry I can’t answer that one.
164 00:35:06.610 ⇒ 00:35:18.909 Deepika Sethi: That’s totally fine, I just wanted to look. Otherwise, also, how does the growth look like? It’s, I first asked in terms of roles or anything, but how does the responsibilities grow, and how those things work, probably, if you have that idea.
165 00:35:18.910 ⇒ 00:35:35.959 Greg Stoutenburg: Yeah, yeah, sure, yeah, I mean, I mean, I think I can speak to that a little bit, just, like, sort of personal experience. I’ve been here for just about 6 weeks, actually, and, there are all sorts of clients, there are all sorts of projects going on, and the team is really good about putting people
166 00:35:35.960 ⇒ 00:35:46.449 Greg Stoutenburg: in areas where they have strengths, and also listening and being responsive to where there’s somewhere that someone wants to grow in. So, you know, they’ll put you on a project that
167 00:35:46.450 ⇒ 00:35:58.020 Greg Stoutenburg: if it… if it fits with something that you want to grow in, and you want to develop in, and there’s… and there is work in that scope, then they’ll say, you know, hey, you know, you take this one. So, and I’ve got work that I’m doing
168 00:35:58.020 ⇒ 00:36:13.639 Greg Stoutenburg: of that sort, you know, actively right now. So, that’s something that’s been really beneficial. You get… you get exposed to… at a consultancy like this, you get exposed to a lot of projects and, lots of different, you know, domains of expertise that you can grow in.
169 00:36:14.160 ⇒ 00:36:35.870 Deepika Sethi: That’s nice to hear. I just had one more question, but I think that, again, might be more of an operations team question, because my wife was also not very sure on it. I’m currently on F1 visa. Even when I applied, I mentioned that I can start from May 12, that’s when CPT starts. I don’t know how will this work out in that case.
170 00:36:35.870 ⇒ 00:36:39.299 Deepika Sethi: Because ideally, I can only do volunteering services for now.
171 00:36:39.600 ⇒ 00:36:40.270 Greg Stoutenburg: Yeah, okay.
172 00:36:40.890 ⇒ 00:36:45.419 Deepika Sethi: Yeah, so that’s what I said, Again, you might not be…
173 00:36:45.540 ⇒ 00:37:04.020 Greg Stoutenburg: I don’t know, but the fact that they’re interviewing you anyway is probably a good sign, so… at least they must think it’s possible. So, yeah, yeah, alright. Yeah, this has been great. Thank you for your time. You know, I’ll give my feedback to the team, and then whoever’s been in touch will be in touch.
174 00:37:04.480 ⇒ 00:37:05.640 Deepika Sethi: Oh, the box!
175 00:37:05.770 ⇒ 00:37:09.899 Greg Stoutenburg: Okay, alright. Thanks a ton, great to meet you. Alright, bye.