Meeting Title: Brainforge Interview w- Amber Date: 2026-02-10 Meeting participants: Chibuzo Nwankwo, Amber Lin
WEBVTT
1 00:10:02.810 ⇒ 00:10:04.460 Amber Lin: Heather, how are you doing?
2 00:10:04.460 ⇒ 00:10:06.510 Chibuzo Nwankwo: I’m fine, are you?
3 00:10:06.880 ⇒ 00:10:13.950 Amber Lin: I’m good! Okay, great to hear that your internet is better. It always happens, so I don’t like it happens.
4 00:10:14.180 ⇒ 00:10:15.940 Chibuzo Nwankwo: Thank you, thank you.
5 00:10:16.200 ⇒ 00:10:17.419 Chibuzo Nwankwo: I said, they’re green.
6 00:10:18.800 ⇒ 00:10:24.100 Amber Lin: It’s going pretty well. It’s about noontime for me now, so…
7 00:10:24.280 ⇒ 00:10:25.250 Chibuzo Nwankwo: Okay.
8 00:10:25.250 ⇒ 00:10:31.210 Amber Lin: So I’m… I’m gonna get lunch after we talk. What about you? What time is it for you?
9 00:10:31.520 ⇒ 00:10:34.230 Chibuzo Nwankwo: It’s, past 9pm.
10 00:10:34.730 ⇒ 00:10:37.320 Amber Lin: Mmm, wow. Where are you based in?
11 00:10:38.100 ⇒ 00:10:39.020 Chibuzo Nwankwo: Nigeria.
12 00:10:39.550 ⇒ 00:10:41.050 Amber Lin: Oh, cool!
13 00:10:44.110 ⇒ 00:10:49.669 Amber Lin: What are you doing right now? Are you… are you looking for work, or are you still working?
14 00:10:50.120 ⇒ 00:10:51.919 Chibuzo Nwankwo: Yes, I’m okay if I work.
15 00:10:52.460 ⇒ 00:10:53.540 Amber Lin: They say, cool.
16 00:10:53.650 ⇒ 00:11:02.019 Amber Lin: Let me pull up… your application. I know you were applying for the…
17 00:11:02.220 ⇒ 00:11:05.950 Amber Lin: Product, or, say, business analyst role.
18 00:11:06.070 ⇒ 00:11:13.250 Amber Lin: Could you tell me what you… what you would like to do, and sort of, like, what your…
19 00:11:16.530 ⇒ 00:11:24.970 Amber Lin: Let’s start it this way, like, Judge, tell me a little bit about, what you do, what you’ve done before, and then I’ll have questions, and we can go in a little deeper.
20 00:11:24.970 ⇒ 00:11:25.560 Chibuzo Nwankwo: Enko.
21 00:11:25.710 ⇒ 00:11:29.119 Chibuzo Nwankwo: So, I’ve worked on several projects, and
22 00:11:29.570 ⇒ 00:11:37.959 Chibuzo Nwankwo: So I’ll just walk you through on the few successful projects. So one of them is, customer satisfaction, auditing.
23 00:11:38.130 ⇒ 00:11:48.459 Chibuzo Nwankwo: So, the objective of the project, the business wants to see how well they have been able to satisfy customer, and how well customers have been patronizing them.
24 00:11:48.670 ⇒ 00:11:53.540 Chibuzo Nwankwo: So, which led them to gain, on an average.
25 00:11:53.740 ⇒ 00:11:57.809 Chibuzo Nwankwo: An Industry CSAT score of 71% and above.
26 00:11:58.180 ⇒ 00:12:04.219 Chibuzo Nwankwo: Then… The tool I use in achieving that project successfully via SQL,
27 00:12:04.530 ⇒ 00:12:17.020 Chibuzo Nwankwo: Excel, and then Power BI, because I created the database, and I use Power BI to, do the ad hoc analysis and visual… show the… they show the insights of
28 00:12:17.080 ⇒ 00:12:32.249 Chibuzo Nwankwo: the metrics and what the business want to talk. And also, I went further to think for the business in terms of the sentiment analysis, whereby emotional-wise, that, why we customers be giving the business such meaning in terms of 3, which is net 12,
29 00:12:32.250 ⇒ 00:12:38.799 Chibuzo Nwankwo: or pull it into all one. So, with that, I propose a recommendation to the business that they should
30 00:12:38.800 ⇒ 00:12:44.900 Chibuzo Nwankwo: Charity campaign, and, also to enlighten the customer about, what do you call it?
31 00:12:44.900 ⇒ 00:13:02.500 Chibuzo Nwankwo: essence of written an order. So, whenever the customer place an order, they should be enlightened about waiting so that they can rate the services which the business rendered to them, which led to copying and driver behavior in terms of reckless driving. That reduced the rate of
32 00:13:02.640 ⇒ 00:13:14.679 Chibuzo Nwankwo: reckless driving from the drivers, driver delivering bad products or damaged goods to the customer. So, these were the recommendations that I proposed to the business, which helped them a lot.
33 00:13:14.970 ⇒ 00:13:18.990 Chibuzo Nwankwo: Then the other project was, customer mapping journey.
34 00:13:19.280 ⇒ 00:13:27.929 Chibuzo Nwankwo: So, the business wants to see how well they have been able to retain more customer. So, what it means is, customer
35 00:13:27.990 ⇒ 00:13:38.589 Chibuzo Nwankwo: may… do we engage customers? Do they want to see the funnel of how customers have been onboarded successfully, if their KYC status was completed?
36 00:13:38.590 ⇒ 00:13:49.090 Chibuzo Nwankwo: And also, if they made the transaction with the business. That helped them to have retention of 70%. And the tools I used in achieving that were SQL and Power BI.
37 00:13:49.090 ⇒ 00:13:58.069 Chibuzo Nwankwo: Then, I also use Python in some of the projects for EDA process, and a little bit of forecasting.
38 00:13:59.570 ⇒ 00:14:01.800 Amber Lin: I see, I hear you. So…
39 00:14:01.830 ⇒ 00:14:21.690 Amber Lin: How would you describe the main, say, business sector that you were working in? Were you mostly in marketing? Were you mostly in, say, customer experience? Were you mostly in the product side? Like, what sector of the business were you in?
40 00:14:21.690 ⇒ 00:14:27.690 Chibuzo Nwankwo: Okay, so, I was in All Grounder, because, our team is centralized.
41 00:14:28.010 ⇒ 00:14:31.199 Chibuzo Nwankwo: So, they made it in a way that you must know.
42 00:14:31.360 ⇒ 00:14:34.440 Chibuzo Nwankwo: The entire business, ecosystem of the business.
43 00:14:34.860 ⇒ 00:14:52.369 Chibuzo Nwankwo: So, I can be pulled into a project that is related to, into product… put into product perspective. I can be put into a project that can be logistic perspective, can be put into a project that can be customer perspective, so it’s like… it’s an all-under.
44 00:14:53.200 ⇒ 00:14:57.089 Chibuzo Nwankwo: Just for me to have a full understanding of the ecosystem of the business.
45 00:14:58.410 ⇒ 00:15:07.169 Amber Lin: I see, are you talking about the company Alertzo, or are you talking about a different company?
46 00:15:07.450 ⇒ 00:15:08.950 Chibuzo Nwankwo: I’m talking about Omnibes.
47 00:15:09.250 ⇒ 00:15:15.590 Amber Lin: Okay, so Omnibiz is a… Could you describe briefly what it does?
48 00:15:15.810 ⇒ 00:15:30.159 Chibuzo Nwankwo: Okay, so Omnibus is a fintech and B2B firm. They… they operate on B2B and B2C mode of operation, so they buy from manufacturers, they sell to manufacturers, they sell to distributors.
49 00:15:30.640 ⇒ 00:15:37.349 Chibuzo Nwankwo: And also, they have three, the business is on two, three parts. One is the retailer.
50 00:15:37.710 ⇒ 00:15:43.849 Chibuzo Nwankwo: 2 is the distributor. Why? And story 2 is a distributor, 3 is the,
51 00:15:43.960 ⇒ 00:15:46.479 Chibuzo Nwankwo: Financial part of the business, which is.
52 00:15:46.480 ⇒ 00:15:46.900 Amber Lin: Indeed.
53 00:15:46.900 ⇒ 00:15:54.289 Chibuzo Nwankwo: OmniPay. So, that part, they give out loans to customers, financial, transaction, and the likes.
54 00:15:54.860 ⇒ 00:16:06.469 Amber Lin: Gotcha, okay, and how do you fit into that business? So, do you help analyze performance for Omnibiz, or do you help… or do you integrate into different clients on… based on what they need?
55 00:16:06.690 ⇒ 00:16:17.410 Chibuzo Nwankwo: Okay, so I work on… I work with… I work with a different part of the business. So, today, let’s say this week, I can be pulled into a project that’s
56 00:16:17.410 ⇒ 00:16:29.919 Chibuzo Nwankwo: who work with, work on, what do they call it, OmniP side of the business. Next week, or tomorrow, I can be put into distributor part of the business. Next tomorrow, or third week, I can be put into
57 00:16:29.920 ⇒ 00:16:38.850 Chibuzo Nwankwo: degree part of the business. So, they just want the entire team, data team, to understand, or to work on projects for us to understand the system.
58 00:16:38.850 ⇒ 00:16:47.000 Amber Lin: I see, I see. So you kind of rotate internally to work on, like, the… Yes. Okay, sounds good.
59 00:16:48.310 ⇒ 00:17:10.049 Amber Lin: Okay, I… I have a general understanding of what you do. I guess my next question is, what… what would motivate you in working? Because people like different things, and I like different things than my colleagues, so I wanted to know what you like, what motivates you, so I, like, we can coordinate inside to see what’s a fit.
60 00:17:12.040 ⇒ 00:17:15.729 Chibuzo Nwankwo: Okay, so what motivated me to apply for LIO is…
61 00:17:15.859 ⇒ 00:17:19.139 Chibuzo Nwankwo: I love to learn new things, I love to explore.
62 00:17:19.300 ⇒ 00:17:32.959 Chibuzo Nwankwo: So, and I love new challenges. So, which is why, this is an opportunity for me to explore, especially more on the AI perspective. I know that I have, I’ve been using AI,
63 00:17:33.530 ⇒ 00:17:44.500 Chibuzo Nwankwo: which has also made my productivity more faster, and also to optimize my code, but I really want to, like, use AI in terms of, like.
64 00:17:44.690 ⇒ 00:17:48.460 Chibuzo Nwankwo: more automation. I think there’s one,
65 00:17:49.070 ⇒ 00:17:54.360 Chibuzo Nwankwo: There’s a learning platform I was… I was undergoing. It’s called, it’s all about
66 00:17:54.470 ⇒ 00:17:59.320 Chibuzo Nwankwo: and it’s N. I think they said it’s a true… for…
67 00:17:59.630 ⇒ 00:18:04.200 Chibuzo Nwankwo: AI automation processes, I believe you know what I’m talking about, NHN.
68 00:18:05.790 ⇒ 00:18:17.140 Chibuzo Nwankwo: Yes. So, which… so, which is why I’m excited about this. So, just to explore more in using AI for automation processes, not just for optimizing code.
69 00:18:18.310 ⇒ 00:18:34.170 Amber Lin: I see. So, you said that, say, learning and explore interests you. Do you… did you do that, or how did you do that at your past job, or how did that… how’d that fit in, or was something else that was also motivating?
70 00:18:35.250 ⇒ 00:18:39.380 Chibuzo Nwankwo: So, there’s nothing else that is motivating me aside from
71 00:18:40.070 ⇒ 00:18:49.899 Chibuzo Nwankwo: what I just mentioned. So, what I’ve used in… currently, or in my past, was, when AI came on board.
72 00:18:50.210 ⇒ 00:18:53.009 Chibuzo Nwankwo: Initially, I wasn’t,
73 00:18:53.550 ⇒ 00:19:12.539 Chibuzo Nwankwo: I wasn’t, how would I put it, like, keen to the idea, but with time, I tend to, like, see the importance of the AI just to make work more faster, rather than if I have a challenge, by going to Stack Overflow, I tend to see the advantage of AI, and also to…
74 00:19:12.540 ⇒ 00:19:26.260 Chibuzo Nwankwo: and give a… be good in prompting, and also to query, like, to be curious about the code. And also, then I realized that I know more better than the AI, just for me to
75 00:19:26.740 ⇒ 00:19:46.700 Chibuzo Nwankwo: give you good prompting that really helps solve my problem, and also to optimize my script and code. So those were the, steps or principles I’ve applied in my job. But my motivation in applying for this one is I want to build, end-to-end projects using an AI automation processes.
76 00:19:52.530 ⇒ 00:20:09.309 Amber Lin: Okay, sounds good. I think, let’s see, we have about 15 minutes, like, I want to make sure at the end I have space for you to ask me as well, so I’ll have about one or two more questions, and you will have time to ask me questions as well.
77 00:20:09.440 ⇒ 00:20:24.919 Amber Lin: So, I think my next question is to… to see what area would you like to work into? What… what kind of is your career trajectory? Where do you want to be in terms of your career?
78 00:20:25.420 ⇒ 00:20:30.439 Chibuzo Nwankwo: Okay, so I see myself in Linnaeus feature to be a data engineer.
79 00:20:30.610 ⇒ 00:20:34.660 Chibuzo Nwankwo: Because, to see myself as a data engineer, because
80 00:20:34.820 ⇒ 00:20:53.230 Chibuzo Nwankwo: My role has really… my Omnibus has really exposed me a lot, where I wear multiple ads, I do analytical engineering, I do data engineering, I do business intelligence, and I do a data analyst. So that has really, made me proud.
81 00:20:53.310 ⇒ 00:21:04.549 Chibuzo Nwankwo: So, which in the nearest future I want to be, will I say, a senior data engineer, yeah? And also to be more advanced in data carrier space.
82 00:21:06.200 ⇒ 00:21:13.090 Amber Lin: So you would like… so you’re saying you want to go to the data engineer route, but…
83 00:21:13.510 ⇒ 00:21:18.950 Amber Lin: right now, I know you’re applying for the product or data analyst role.
84 00:21:18.950 ⇒ 00:21:19.870 Chibuzo Nwankwo: And so…
85 00:21:19.870 ⇒ 00:21:22.599 Amber Lin: How do you see that transition?
86 00:21:23.550 ⇒ 00:21:30.160 Chibuzo Nwankwo: So, I believe that in the… what do they call it, in this role that I applied for.
87 00:21:30.390 ⇒ 00:21:33.949 Chibuzo Nwankwo: In a space of, let’s say, 1 or 2 years.
88 00:21:34.150 ⇒ 00:21:43.900 Chibuzo Nwankwo: If I’m successfully shortlisted, then I will be able to, transition fully, because I fully understood the business.
89 00:21:46.910 ⇒ 00:21:53.060 Amber Lin: I see. Let’s see… Okay.
90 00:21:53.160 ⇒ 00:22:00.200 Amber Lin: So, if you were to come on board and do, say, product analy…
91 00:22:00.360 ⇒ 00:22:07.779 Amber Lin: analystwork, do you have any, say, experience with experimentation, or…
92 00:22:08.300 ⇒ 00:22:26.479 Amber Lin: like, how you did specific analysis work, because right now, we have data engineers, and I think it’s possible to transition within the organization, but currently, we are hiring for an analyst, so I wanted to see your ability for that role currently.
93 00:22:26.480 ⇒ 00:22:27.200 Chibuzo Nwankwo: Okay.
94 00:22:27.670 ⇒ 00:22:39.799 Chibuzo Nwankwo: Okay, so, I’m available for the… for the role, which is what I applied for, and also, for the product analysis, I have an experience in it, because, Omnibus…
95 00:22:39.800 ⇒ 00:22:48.550 Chibuzo Nwankwo: I work on a project related to product analysis, whereby, we did an, what they call it, A-B testing.
96 00:22:48.550 ⇒ 00:23:03.570 Chibuzo Nwankwo: Just to see the previous version of the products, how well customers are… how well customers have been purchasing their products, or the… buying the products at that, and the new version of the products, just to compare and see…
97 00:23:03.570 ⇒ 00:23:21.740 Chibuzo Nwankwo: the retention of, what do you call it, customer purchases towards those, products. So, that has helped the business in using the right version for the, for the product, and also products that are not moving. So, also, that leads to, what would they call it? I think they call it four categories of,
98 00:23:21.900 ⇒ 00:23:26.729 Chibuzo Nwankwo: And products, slow-moving, slow-moving products, slow-moving with,
99 00:23:26.880 ⇒ 00:23:35.040 Chibuzo Nwankwo: slow moving and low price. We have slow moving and high price. We have,
100 00:23:35.060 ⇒ 00:23:47.019 Chibuzo Nwankwo: high-moving SKU and, low price, high-moving SKU and, high price. So this built in the constitution of this, bucket, mechanism.
101 00:23:52.230 ⇒ 00:23:52.790 Amber Lin: Huh?
102 00:23:53.230 ⇒ 00:23:56.290 Amber Lin: Sounds good. I’ll note that down.
103 00:23:56.480 ⇒ 00:24:00.819 Amber Lin: Let’s see. Any questions you have for me?
104 00:24:01.370 ⇒ 00:24:02.080 Chibuzo Nwankwo: Yes.
105 00:24:02.220 ⇒ 00:24:05.070 Chibuzo Nwankwo: So, I want to know why the wall is vacant.
106 00:24:06.870 ⇒ 00:24:07.550 Amber Lin: Pardon me?
107 00:24:07.960 ⇒ 00:24:10.150 Chibuzo Nwankwo: So, I want to know why the wall is vacant.
108 00:24:11.320 ⇒ 00:24:30.740 Amber Lin: The role is vacant. I see. So, we are an expanding company, so I’ve joined about a year ago now, and our client base has increased significantly, so we are… we operate in a consulting model, and our data analysts work on client projects, so…
109 00:24:30.740 ⇒ 00:24:31.220 Chibuzo Nwankwo: Okay.
110 00:24:31.220 ⇒ 00:24:45.640 Amber Lin: It’s slightly different, I think, from Omnibiz, that you described, where the data analyst team works on the internal analysis. So, we staff people, like a consultant, so we staff them on projects.
111 00:24:45.640 ⇒ 00:24:52.630 Amber Lin: So, because we have more clients, then we need more people to come on to the projects.
112 00:24:53.100 ⇒ 00:24:53.830 Chibuzo Nwankwo: Okay.
113 00:24:54.790 ⇒ 00:25:01.959 Chibuzo Nwankwo: So, my second question is… yes. So, what those sources look like, in the next 6 months?
114 00:25:04.100 ⇒ 00:25:09.250 Amber Lin: So what… to repeat your question, what does the role look like in this example?
115 00:25:09.250 ⇒ 00:25:09.580 Chibuzo Nwankwo: So.
116 00:25:09.700 ⇒ 00:25:18.440 Chibuzo Nwankwo: Now, what does success, like, if I’m shortlisted, what does success look like in 6 months, or let me say 12 months?
117 00:25:19.680 ⇒ 00:25:37.619 Amber Lin: I see. So, I would say we ramp people up very quickly, so when you get… when you get onboarded, I think you would become on… you would come on to a client project, within the first month, or within the first two weeks, and so success means that
118 00:25:37.620 ⇒ 00:25:51.630 Amber Lin: one, tasks assigned to you are completed well. Of course, there will be guidance, and there’ll be more senior people, or people, or, say, the project leader will check the work. So, meaning that work is done well.
119 00:25:51.630 ⇒ 00:25:59.299 Amber Lin: That the relation… there’s a good relationship with the client. The client likes working with you. The client is,
120 00:25:59.680 ⇒ 00:26:03.199 Amber Lin: Satisfied by the work, and then…
121 00:26:03.300 ⇒ 00:26:19.110 Amber Lin: So I think that would be, a very good performance, and I think an extraordinary performance would be, one, say, the contract with the client renewals, or we ended up with a bigger contract, so, if there’s additional…
122 00:26:19.110 ⇒ 00:26:27.340 Amber Lin: So the contract grows with the client? Or, say, you were able to take on additional responsibilities.
123 00:26:27.340 ⇒ 00:26:44.039 Amber Lin: And of course, there’s internal resources to help with that. So, for example, you wanted to automate a workflow, then that’s an extra thing that we’ll look out for, and I think internally, we have bonuses when people, automate work, when people create playbooks.
124 00:26:44.040 ⇒ 00:26:49.879 Amber Lin: When people help recruit, or when people help upsell, so I think,
125 00:26:50.230 ⇒ 00:27:09.120 Amber Lin: one, the first level of success is doing your job really well, and the client loves you, the team loves working with you, and the next level is, contributions to the company, contributions to, sales, etc. So, that’s how I would describe success.
126 00:27:10.840 ⇒ 00:27:17.730 Chibuzo Nwankwo: Okay, so my third question is, what tool will I be working with that will make my work more effective and efficient?
127 00:27:19.750 ⇒ 00:27:29.279 Amber Lin: Gotcha, so you’re asking what tools would make your current work more effective, or work at our company more effective?
128 00:27:29.590 ⇒ 00:27:33.650 Chibuzo Nwankwo: Yes, so what else will I be working with?
129 00:27:33.810 ⇒ 00:27:36.830 Chibuzo Nwankwo: That will make my work more productive.
130 00:27:37.060 ⇒ 00:27:38.310 Chibuzo Nwankwo: Or effective.
131 00:27:39.920 ⇒ 00:27:47.640 Amber Lin: Gotcha, okay. So I would say, I would say when I do the work, there’s two sets of tools we’re using, so there’s
132 00:27:47.780 ⇒ 00:27:54.300 Amber Lin: tools in terms of managing tasks, managing communications, and I would call them operational tools.
133 00:27:54.330 ⇒ 00:28:10.740 Amber Lin: And then there’s tools that I do my work with, so that would be, say, if you’re working within Excel, if you’re working with a BI tool, if you’re working in a data warehouse, etc. So I think there’s two sets of tools,
134 00:28:11.020 ⇒ 00:28:23.779 Amber Lin: I think the tools you work with are pretty standard, like, that depends on the client of what kind of data warehouse they’re using, what kind of dashboards they’re using, so that depends on them.
135 00:28:23.830 ⇒ 00:28:35.130 Amber Lin: Okay. I think internally, the operational tools we have are project management tools, we have… I think that’s a big perk of our company, is that
136 00:28:35.130 ⇒ 00:28:46.629 Amber Lin: We are very AI automated, and we have a lot of things stored in context, so when you are onboarded to a project, it’s very fast, and you’ll be able to answer a lot of questions.
137 00:28:46.630 ⇒ 00:29:00.689 Amber Lin: using the existing context, and you’ll know very clearly, okay, for a test I need to do, what has been said about it? Where do I find the data? So we have good documentation.
138 00:29:00.840 ⇒ 00:29:04.799 Amber Lin: And a set of AI tools to reference past meetings, past…
139 00:29:04.840 ⇒ 00:29:13.440 Amber Lin: test messages, so I think that would help make your work a lot more efficient. And in terms of your actual work.
140 00:29:13.440 ⇒ 00:29:30.379 Amber Lin: You’ll be very integrated with AI when you do your analysis, when you do your structure, etc. So, if you were to come on board, we will walk you through the set of AI tools that we develop internally for these processes, and I think that will be very helpful.
141 00:29:31.970 ⇒ 00:29:34.289 Chibuzo Nwankwo: Thank you. I think that answers my questions.
142 00:29:34.750 ⇒ 00:29:46.099 Amber Lin: Yeah, awesome. I think we have about 8 minutes. I would love to ask you a bit more about, what the working environment was like, or,
143 00:29:46.360 ⇒ 00:29:48.420 Amber Lin: Who… let’s see…
144 00:29:48.770 ⇒ 00:29:59.549 Amber Lin: So my first question would be, say, who was your last boss, and how would he rate you on a scale of, say, 1 to 10?
145 00:30:00.890 ⇒ 00:30:05.800 Chibuzo Nwankwo: Okay, so my last boss is, she was a…
146 00:30:05.970 ⇒ 00:30:06.330 Amber Lin: Hmm.
147 00:30:06.330 ⇒ 00:30:13.590 Chibuzo Nwankwo: Head of business, and a data manager. So, on a scale of 10, she’ll rate me 7.
148 00:30:14.320 ⇒ 00:30:15.680 Amber Lin: Why is that?
149 00:30:16.530 ⇒ 00:30:26.309 Chibuzo Nwankwo: So, because, I’m productive, and active, and, I love to collaborate, and,
150 00:30:27.090 ⇒ 00:30:40.169 Chibuzo Nwankwo: I love to… I don’t want to say no, I love to, like… when I’m being called upon on a project, I love to give positive attitude towards it.
151 00:30:43.120 ⇒ 00:30:52.019 Amber Lin: I mean, that sounds… that sounds really awesome. Why would she give you a 7 instead of a 10? That sounds like an awesome, like, awesome person to work with.
152 00:30:52.380 ⇒ 00:31:11.510 Chibuzo Nwankwo: Yeah, so, you know, we are, we are much on a team. I think we are 20, 20, personnel, I say, in the data team. We have the data engineers, the data scientists, the business intelligence, the data analysts, and the junior data analyst. So, we make sure that all, we all collaborate together.
153 00:31:13.180 ⇒ 00:31:29.829 Chibuzo Nwankwo: Yeah, so, and, your project that you deliver successfully, which, gave AOI for the business and also generated revenue and customer satisfaction, that’s because I could remember one of my projects.
154 00:31:29.830 ⇒ 00:31:41.809 Chibuzo Nwankwo: help the business. With that CSAT score alone, like, the business was really a mess for them to gain, on an average, 71% above of CSAT score. That was really impressive for the business.
155 00:31:46.700 ⇒ 00:31:50.849 Amber Lin: I see. I think I was more so asking, like, why was she… why would.
156 00:31:50.850 ⇒ 00:31:51.339 Chibuzo Nwankwo: Do not…
157 00:31:51.340 ⇒ 00:32:00.149 Amber Lin: give you a 7… why would she give you a 7 instead of a 10? Like, why would she give you a lower score than perfect, was my question.
158 00:32:00.150 ⇒ 00:32:11.099 Chibuzo Nwankwo: Okay, so, we rarely see… we rarely see her give anybody the same 10 over 10, so, like, 10 over 10 sounds like…
159 00:32:11.240 ⇒ 00:32:12.360 Chibuzo Nwankwo: Like…
160 00:32:12.460 ⇒ 00:32:22.329 Chibuzo Nwankwo: I don’t know the word to use, like, that’s our own perspective, so… at least 7 is still… 7 is, like, 10 over 10, so if you can’.
161 00:32:22.330 ⇒ 00:32:27.409 Amber Lin: I see, okay, okay, okay, I see you. So you are a 10 out of 10, she would say that.
162 00:32:27.410 ⇒ 00:32:28.370 Chibuzo Nwankwo: What’s her name?
163 00:32:28.370 ⇒ 00:32:30.509 Amber Lin: Can I, can I write her name down?
164 00:32:31.120 ⇒ 00:32:34.610 Chibuzo Nwankwo: Sayama.
165 00:32:36.460 ⇒ 00:32:38.879 Chibuzo Nwankwo: CMA. Yeah, CMA.
166 00:32:39.940 ⇒ 00:32:42.340 Amber Lin: Gotcha, okay.
167 00:32:44.370 ⇒ 00:32:55.480 Amber Lin: Awesome. How was your co-workers? Were you leading a team of, say, were there, like, analysts below you? Because I saw you were a senior analyst. Like, did you have a team there?
168 00:32:55.480 ⇒ 00:32:55.880 Chibuzo Nwankwo: Yeah.
169 00:32:55.880 ⇒ 00:32:56.810 Amber Lin: That seat?
170 00:32:58.960 ⇒ 00:33:12.269 Chibuzo Nwankwo: Yes, so I have 5 analysts that report to me. They are junior analysts, so they report to me, and I also monitor the project that is being assigned to them to make sure that they complete on or before deadline.
171 00:33:12.790 ⇒ 00:33:22.879 Amber Lin: Okay. How do you manage them? Like, do you assign them tasks and kind of break it down for them, or do you, like, check their work? How does that work for you?
172 00:33:23.070 ⇒ 00:33:36.620 Chibuzo Nwankwo: Okay, so I check their work. So, the manager assigns tasks to them, then sometimes the manager, pull me in to support them in their projects, just to guide them.
173 00:33:37.780 ⇒ 00:33:44.360 Amber Lin: I see, I see. Gotcha, that seems like a really, like.
174 00:33:44.640 ⇒ 00:33:51.150 Amber Lin: they really value your expertise to have you manage these 5 people, so I’ll also note that down.
175 00:33:51.220 ⇒ 00:34:09.320 Amber Lin: I think that would be all the questions I have for now. I think the next step would be, I will… I would send this to my… my team, they’ll evaluate, and the operations team will get back to you, with the next steps.
176 00:34:09.480 ⇒ 00:34:20.469 Amber Lin: And so, I think that they should get back to you within, say, 2 weeks. If not, feel free to email them, and then they will… they will check the results for you.
177 00:34:24.780 ⇒ 00:34:28.389 Chibuzo Nwankwo: Okay. I look forward to that. I look forward to working with you.
178 00:34:28.830 ⇒ 00:34:42.259 Amber Lin: Okay, awesome. Thank you so much for taking the time, and thank you for rebooking. I know, like, it was a, it was not the smoothest process, but thank you for sharing your experience. I really appreciate it.
179 00:34:45.520 ⇒ 00:34:48.199 Chibuzo Nwankwo: Thank you, thank you. I look forward to work with you.
180 00:34:48.909 ⇒ 00:34:51.960 Amber Lin: I appreciate that. Alright. Bye!
181 00:34:51.960 ⇒ 00:34:54.170 Chibuzo Nwankwo: Bye! Bye!
182 00:34:54.179 ⇒ 00:34:55.219 Amber Lin: Have a good one.
183 00:34:55.520 ⇒ 00:34:56.950 Chibuzo Nwankwo: Thank you, you too.