Meeting Title: Product Analytics Role Discussion Date: 2025-08-18 Meeting participants: Amber Lin, Shreya Chowdhury
WEBVTT
1 00:04:26.950 ⇒ 00:04:27.800 Shreya Chowdhury: Hello?
2 00:04:32.970 ⇒ 00:04:36.220 Amber Lin: Hi there. Sorry, I was a bit late to the… to the meeting.
3 00:04:36.220 ⇒ 00:04:38.949 Shreya Chowdhury: No, no worries. How’s it going?
4 00:04:39.240 ⇒ 00:04:41.899 Amber Lin: Pretty good, it’s been a busy, busy day.
5 00:04:42.780 ⇒ 00:04:44.480 Amber Lin: Are you working right now?
6 00:04:44.790 ⇒ 00:04:52.820 Shreya Chowdhury: Currently, no. I have been not working for the past, like, few weeks.
7 00:04:52.820 ⇒ 00:04:53.520 Amber Lin: Huh.
8 00:04:53.860 ⇒ 00:04:56.080 Shreya Chowdhury: Yeah, so, just kind of, like.
9 00:04:56.390 ⇒ 00:05:02.019 Shreya Chowdhury: exploring new opportunities slowly, but yeah, that’s where I am right now.
10 00:05:02.020 ⇒ 00:05:08.730 Amber Lin: Cool, I see. I heard really great things about you from Utam and then from Robert. You’ve talked to both of them already, right?
11 00:05:08.730 ⇒ 00:05:11.739 Shreya Chowdhury: Yeah, yeah, I talked to both of them. That’s really nice to hear.
12 00:05:12.180 ⇒ 00:05:21.940 Amber Lin: Yeah, what… well, I know they’re interviewing for product analytics. Is that the direction you want to go towards?
13 00:05:22.250 ⇒ 00:05:36.270 Shreya Chowdhury: Yeah, so I was hoping that we could take some time in this call to figure out, like, exactly what the best placement for me would be, because I know they both mentioned that there is, like, a lot of different spaces that I could be working in.
14 00:05:36.310 ⇒ 00:05:42.839 Shreya Chowdhury: I guess I’m just curious. I… I like product analytics, I think that is, like, my strongest area. I’m…
15 00:05:42.890 ⇒ 00:05:44.600 Shreya Chowdhury: open to…
16 00:05:45.030 ⇒ 00:05:54.499 Shreya Chowdhury: other roles too, but I probably don’t want to start with one of the engineering roles right now, because I think I’m more interested in the product analytics space.
17 00:05:54.500 ⇒ 00:05:55.020 Amber Lin: Hmm.
18 00:05:55.480 ⇒ 00:06:01.629 Shreya Chowdhury: But, yeah, like, what Thumb said that I could talk to you, and see, like.
19 00:06:01.990 ⇒ 00:06:06.079 Shreya Chowdhury: what specific roles are available and where I might be best placed.
20 00:06:06.390 ⇒ 00:06:08.469 Amber Lin: Huh, sure. Well…
21 00:06:08.630 ⇒ 00:06:20.110 Amber Lin: I was trying… I was looking for their notes. I don’t have their notes here, so apologies if I asked duplicated questions, because I don’t know where… what they have asked you before.
22 00:06:20.110 ⇒ 00:06:32.630 Amber Lin: So from my understanding, looking at your LinkedIn, your experience was in, so from data scientists, and then to doing analytics, mostly for product analytics, right?
23 00:06:33.040 ⇒ 00:06:33.600 Shreya Chowdhury: Yeah.
24 00:06:33.600 ⇒ 00:06:37.969 Amber Lin: I see. And when you say engineering roles, what are you referring to?
25 00:06:38.190 ⇒ 00:06:40.960 Shreya Chowdhury: I… I don’t… so, I have…
26 00:06:41.350 ⇒ 00:06:57.590 Shreya Chowdhury: at my last job, I was basically full stack, so we did, like, data analytics, data engineering, everything in between, and that was a lot of, like, building, like, data pipelines and making sure that, like, we have, like, an internal good data foundation set up.
27 00:06:57.590 ⇒ 00:07:03.640 Shreya Chowdhury: And Utham said that he works a little bit more on the data engineering side, and he likes that space.
28 00:07:04.050 ⇒ 00:07:14.430 Shreya Chowdhury: I think it’s an interesting space to explore, too, but I think a lot of the other projects that he was talking about where, like, we would get new clients, and you’d be kind of, like.
29 00:07:14.430 ⇒ 00:07:24.820 Shreya Chowdhury: either troubleshooting or, like, diagnosing, like, the problems that they come to you with, and then using data to drive those solutions. I think that space is more interesting.
30 00:07:24.820 ⇒ 00:07:32.189 Shreya Chowdhury: But I’m definitely open to… Like, figuring out exactly what the best placement for me would be.
31 00:07:32.190 ⇒ 00:07:37.519 Amber Lin: Totally. I think on the project, because we usually also handle full-stack
32 00:07:37.520 ⇒ 00:07:54.179 Amber Lin: For the clients, and then there’s full… and then there’s specific projects, there’s more DE-heavy, and there’s more analytics-heavy projects. I think currently we have UTAM, we have Awish, they’re both data engineers,
33 00:07:54.180 ⇒ 00:08:14.960 Amber Lin: and they’re more full-stack, but focused on data engineering. We have someone new who’s more DE and AE, also on the… so on the earlier side of the pipeline. And I think what we’re lacking right now is we have a analyst, but she’s very focused on dashboarding, so there’s limited…
34 00:08:14.960 ⇒ 00:08:34.490 Amber Lin: analytics, limited insights or strategic recommendations, or hypothesis tests that she can do, so she’s very focused on dashboarding. I think that’s a gap that Robert has been filling, but now that we’re growing and we want to give Robert more time, that’s something that we’re looking for someone to help out.
35 00:08:34.760 ⇒ 00:08:35.490 Shreya Chowdhury: Okay.
36 00:08:35.960 ⇒ 00:08:51.239 Shreya Chowdhury: Yeah, that sounds like a good space to work in. I have experience with hypothesis testing and experimentation, and, like, product recommendations, so I think, yeah, that could be… I could be interested in filling that role.
37 00:08:51.240 ⇒ 00:08:51.780 Amber Lin: Hmm.
38 00:08:52.000 ⇒ 00:09:06.790 Amber Lin: Yeah, and, I know you were asking me to see where… what opportunities exist, that you can fill. It depends project by project, because each client’s needs are different, but I know that’s something you’re…
39 00:09:06.790 ⇒ 00:09:18.169 Amber Lin: familiar with. I think, in general, I think our biggest gap is Analytics… analytical insights, and…
40 00:09:18.280 ⇒ 00:09:36.939 Amber Lin: communicating with that client of, hey, this is what we can find, because we can build… we have good people, we have good DEs and AEs that can build the data and build the pipelines and the models, but if you were to ask me, I think the most important part is with the analytical insights.
41 00:09:37.970 ⇒ 00:09:38.800 Shreya Chowdhury: Okay.
42 00:09:40.420 ⇒ 00:09:51.100 Shreya Chowdhury: Yeah, I think… yeah, that sounds cool. That’s a space that I’m familiar in, that’s, like, what the bulk of my work has been for the last, like, 6 to 8 months.
43 00:09:51.530 ⇒ 00:09:58.779 Shreya Chowdhury: it’s… it’s pretty fresh and familiar to me still. But yeah, I think that sounds like a good space for me to be in.
44 00:09:59.130 ⇒ 00:10:06.529 Amber Lin: Hi, I wanted to ask a little bit about what working style you’re used to.
45 00:10:06.720 ⇒ 00:10:14.280 Amber Lin: So that could mean, like, what type of communicat… what are communications like on the team? Who owns, maybe does…
46 00:10:14.880 ⇒ 00:10:26.159 Amber Lin: Is there a PM on your project? What’s the size of the projects? Who owns communication? Who owns, like, delivering? What is it like, and what are… what do you prefer?
47 00:10:26.560 ⇒ 00:10:29.349 Shreya Chowdhury: Yeah, so what I’ve…
48 00:10:29.350 ⇒ 00:10:54.300 Shreya Chowdhury: In my previous job, we had embedded teams, so basically there would be a team of, like, data scientists, and then we would have our cross-functional project team. So, like, when we take on, like, a feature launch or a specific project or whatever, they would outsource, like, one person from the data team to own each space. So, like, on the project team, there would be, like, one project manager, there would be, like, one to
49 00:10:54.300 ⇒ 00:11:09.800 Shreya Chowdhury: to, UX designers, three developers, and one data scientist, usually. And then in that team, you’re kind of, like, wearer of all hats, so, like, whatever people need you to do, like, whether it’s putting out fires or, like, building a new data foundation,
50 00:11:09.800 ⇒ 00:11:18.770 Shreya Chowdhury: getting, like, analytics to support or reject a decision or drive those decisions. That was kind of my job.
51 00:11:19.370 ⇒ 00:11:31.680 Shreya Chowdhury: some of it was, like, yeah, gathering data before they would ship a feature, or, like, looking at a lot of the data afterwards to analyze if that feature shipment was successful. …
52 00:11:31.680 ⇒ 00:11:46.890 Shreya Chowdhury: My stakeholders for data, my stakeholders was basically everybody, like, from… including the project manager and also, like, the developers, because sometimes they would rely on the data that we bring them before they, like, work on a feature or something like that.
53 00:11:47.040 ⇒ 00:11:53.540 Shreya Chowdhury: So that’s kind of the style that I’m used to. I…
54 00:11:54.550 ⇒ 00:11:56.730 Shreya Chowdhury: I feel like it was a very, like…
55 00:11:58.930 ⇒ 00:12:16.699 Shreya Chowdhury: you have to be ready to, like, do whatever people ask of you to get the project across the finish line, but it was also very, like… I think it really depended on the project. Some projects were just, like, triage, and other ones were more like, okay, like, you can start from scratch with the data and, like, drive
56 00:12:16.700 ⇒ 00:12:19.670 Shreya Chowdhury: Like, the meaningful product decisions. …
57 00:12:19.670 ⇒ 00:12:22.600 Shreya Chowdhury: It really would just depend project to project.
58 00:12:23.620 ⇒ 00:12:25.500 Amber Lin: I see. …
59 00:12:26.920 ⇒ 00:12:42.089 Amber Lin: I… because I… because I know when you were at Shopify, it’s mostly, stakeholders was the people in the company and the customer, so there’s not directly, say, a client that you’re working with.
60 00:12:42.090 ⇒ 00:12:49.349 Shreya Chowdhury: Not the team that I worked on, no. I didn’t have a lot of client-facing work. I worked…
61 00:12:49.350 ⇒ 00:13:07.890 Shreya Chowdhury: like, hand-in-hand with, like, on our team, there would be, like, like, a researcher or someone, or, like, a partner manager, and that person was very client-facing, so, like, they would, basically sync with the clients, whether that be the developers or the merchants or whoever, and then,
62 00:13:08.310 ⇒ 00:13:16.580 Shreya Chowdhury: they would bring that back to us, and then we would, like, figure out how to solve those decisions. But I didn’t directly interact with any of the partners.
63 00:13:16.580 ⇒ 00:13:23.340 Amber Lin: Okay, I see. What type of culture or team dynamics do you prefer?
64 00:13:24.190 ⇒ 00:13:29.440 Shreya Chowdhury: I think I like a collaborative space, so I…
65 00:13:31.910 ⇒ 00:13:37.810 Shreya Chowdhury: Basically, in our data science team, there was more of a, like.
66 00:13:38.250 ⇒ 00:13:48.349 Shreya Chowdhury: cut and dry of, like, what roles and responsibilities you would have, depending on your level and craft. But in the cross-functional teams, it was kind of like…
67 00:13:49.180 ⇒ 00:14:07.110 Shreya Chowdhury: you’d just be ready to do whatever needs to get done, and it’s like, you would be working with, like, senior developers, staff developers, like, whatever, and it’s like, they were still relying on, like, me, like, whether I was entry level or, like, even after I had been, like.
68 00:14:07.380 ⇒ 00:14:10.499 Shreya Chowdhury: when I think I was an L5, like, it was still, like.
69 00:14:10.910 ⇒ 00:14:15.849 Shreya Chowdhury: Your voice is weighted for, like, the expertise that you bring, not the level that you’re at.
70 00:14:16.130 ⇒ 00:14:18.879 Amber Lin: Oh, I see, I hear you. …
71 00:14:19.790 ⇒ 00:14:27.489 Amber Lin: I mean, that’s most of my questions. I’m not responsible for technically screening you, I think that was Robert… what Robert was doing.
72 00:14:27.490 ⇒ 00:14:28.250 Shreya Chowdhury: ….
73 00:14:28.250 ⇒ 00:14:32.010 Amber Lin: And so I just want to make sure that I can also help answer all your questions.
74 00:14:32.210 ⇒ 00:14:32.860 Shreya Chowdhury: Yeah.
75 00:14:33.060 ⇒ 00:14:41.940 Shreya Chowdhury: I think all three of you guys did a great job of answering most of my questions, like, as I came in, so I don’t have a lot of remaining ones.
76 00:14:41.940 ⇒ 00:14:42.530 Amber Lin: Kind of.
77 00:14:42.530 ⇒ 00:14:44.140 Shreya Chowdhury: sides, …
78 00:14:45.240 ⇒ 00:14:52.780 Shreya Chowdhury: yeah, like, I guess, like, where exactly I would be placed, but I don’t know if that comes later, or, like, that’s something you figure out after this meeting or not.
79 00:14:53.100 ⇒ 00:14:55.160 Amber Lin: So I think if…
80 00:14:55.340 ⇒ 00:15:03.819 Amber Lin: if you were to be, the analyst and helping on product analytics and helping on other things, I think you would…
81 00:15:03.860 ⇒ 00:15:23.450 Amber Lin: probably be floating around different projects as they… as they need you, because not… we don’t always need, an analyst to always sit on the call with the, to sit on the stand-ups, to sit in the different client calls, if we don’t have a need for that. Based on what I read in…
82 00:15:23.750 ⇒ 00:15:38.449 Amber Lin: In our internal slacks, I think they want you to float across different roles and float across different clients. Is that something that you would like to do, or does that feel like you don’t have an anchor?
83 00:15:38.450 ⇒ 00:15:39.100 Shreya Chowdhury: Honestly.
84 00:15:39.100 ⇒ 00:15:39.500 Amber Lin: I had a.
85 00:15:39.500 ⇒ 00:15:48.889 Shreya Chowdhury: I think I would prefer that. I think it keeps the work dynamic and interesting, and then I get to do a lot of things, or try a lot of different things.
86 00:15:48.890 ⇒ 00:15:50.160 Amber Lin: I….
87 00:15:50.210 ⇒ 00:15:54.349 Shreya Chowdhury: I think I’m used to that working style, and I honestly prefer it.
88 00:15:54.350 ⇒ 00:15:54.970 Amber Lin: Hmm.
89 00:15:54.970 ⇒ 00:15:55.710 Shreya Chowdhury: ….
90 00:15:55.710 ⇒ 00:15:59.770 Amber Lin: You deal with contact switching, then? Because that’s a lot of different
91 00:16:00.030 ⇒ 00:16:03.610 Amber Lin: Clients and their whole background to handle.
92 00:16:04.160 ⇒ 00:16:14.290 Shreya Chowdhury: Yeah, I think the thing with that is, like, I feel like it all comes down to, like, okay, you’re given, like, a list of problems, and you come up with a list of solutions. Like, I don’t think…
93 00:16:16.230 ⇒ 00:16:33.740 Shreya Chowdhury: I think, like, having an anchor is nice, and it’s nice to specialize, but I think I prefer, like, a more dynamic working environment. Like, that’s what I personally thrive in, and I think it kept, like, my last job, like, very interesting, because that’s, like, kind of the style that you were in, so it’s, like.
94 00:16:33.740 ⇒ 00:16:39.549 Shreya Chowdhury: You’re always doing something more or less with data or related to data, but you kind of just don’t know, like.
95 00:16:39.820 ⇒ 00:16:45.360 Shreya Chowdhury: How you’re going to leverage it, what way you’re going to leverage it, what you’re going to be solving with it.
96 00:16:45.360 ⇒ 00:16:45.970 Amber Lin: Hmm.
97 00:16:47.230 ⇒ 00:16:48.260 Shreya Chowdhury: Yeah. Go ahead.
98 00:16:48.570 ⇒ 00:16:49.410 Amber Lin: I see.
99 00:16:49.840 ⇒ 00:16:52.620 Amber Lin: Okay. Any other questions you have?
100 00:16:52.770 ⇒ 00:17:04.510 Amber Lin: I can also show you what, … I can also show you our internal AI platform, but I don’t know how much it relates to your daily work. I could tell you….
101 00:17:04.530 ⇒ 00:17:11.899 Shreya Chowdhury: No, sure, I’d love… I’d love to see it, if you can. Also, let me know if there’s too much background noise, it’s, like, extra noisy out here today.
102 00:17:11.900 ⇒ 00:17:17.229 Amber Lin: It’s fine for me, I don’t, I don’t hear anything. Zoom does a really good job.
103 00:17:18.819 ⇒ 00:17:22.069 Amber Lin: Are you guys allowed to use AI at Shopify? I know some companies.
104 00:17:22.079 ⇒ 00:17:31.259 Shreya Chowdhury: Oh, like, we were… they’re very, very bullish on AI, so we were highly, highly encouraged to use it. We had multiple different models, and it was, like.
105 00:17:31.439 ⇒ 00:17:39.699 Shreya Chowdhury: they would praise you for it. So, like, if you were using it a lot, you would get, like, the notification you’re, like, the top whatever percentile of users, and it was, like.
106 00:17:39.700 ⇒ 00:17:40.550 Amber Lin: That’s so funny.
107 00:17:40.550 ⇒ 00:17:41.800 Shreya Chowdhury: Gods of honor, yeah.
108 00:17:41.800 ⇒ 00:17:48.439 Amber Lin: Because that’s what… that’s what I’m trying to do for one of their clients, because we’re developing an AI system for them.
109 00:17:48.590 ⇒ 00:18:04.180 Amber Lin: And usage is a big headache, and it’s the same thing that you can do to boost usage. You give people leaderboards, you give them little star stickies, same thing, I’ve gotta praise them if they use it more, and I… it sounds like it was successful there.
110 00:18:04.740 ⇒ 00:18:08.009 Shreya Chowdhury: Yeah, I mean, I think, like, it really did a good job of, like.
111 00:18:08.930 ⇒ 00:18:12.429 Shreya Chowdhury: catalyzing a lot of the workflows, like, I think it was…
112 00:18:12.540 ⇒ 00:18:23.290 Shreya Chowdhury: I think it has a lot of good applications. I try to have, like, some level of discernment, because I feel like when you start relying on it too heavily, like, you lose, like.
113 00:18:23.760 ⇒ 00:18:31.400 Shreya Chowdhury: you lose, like, some context, like, there’s some things that, like, you can’t teach it, or, like, you have to verify it after, like…
114 00:18:31.530 ⇒ 00:18:39.110 Shreya Chowdhury: I think it should be, like, a good… like, I really like it as a pair programmer, like, to help me, like, debug and, like, do the things that, like, would take me hours to do.
115 00:18:39.210 ⇒ 00:18:40.330 Shreya Chowdhury: ….
116 00:18:40.330 ⇒ 00:18:42.709 Amber Lin: Let’s see, what tools do you use, and how do you use it?
117 00:18:42.980 ⇒ 00:18:44.980 Shreya Chowdhury: So, like.
118 00:18:45.700 ⇒ 00:19:00.839 Shreya Chowdhury: for work-related stuff, it would be, like, okay, if I’m, like, writing an analysis document, I would use it to help me, like, come up with an outline, so what’s, like, a good structure? Help me come up with this. And then…
119 00:19:01.410 ⇒ 00:19:13.500 Shreya Chowdhury: I, like, from the bullet… like, what I would do is, like, I would try to, like, I’ll, like, summarize the findings in my own words, and then I would plug it into GPT and be like, hey, can you make this sound a little bit nicer? But then, like.
120 00:19:13.910 ⇒ 00:19:23.489 Amber Lin: it’s kind of like a sandwich method, it’s like, I’d write it in my own words, feed it into that, it comes out with something, like, super AI, then, like, you kind of rewrite that one.
121 00:19:23.490 ⇒ 00:19:27.840 Shreya Chowdhury: But I think that back and forth helps. I like using it for, like.
122 00:19:28.290 ⇒ 00:19:33.389 Amber Lin: if I was writing really long SQL queries with, like, a lot of CTEs.
123 00:19:33.620 ⇒ 00:19:50.819 Shreya Chowdhury: and it’s not running because of a syntax error, it’s hard to go through, like, 100 lines of SQL to find exactly the syntax error, so I would just plug it in and be like, hey, can you, like, format this for me nicely, or, like, fix the syntax issue? Like, that one would actually save me hours, like.
124 00:19:51.810 ⇒ 00:19:56.350 Shreya Chowdhury: Have you tried using it to help you write the code?
125 00:19:57.160 ⇒ 00:20:05.969 Shreya Chowdhury: Yeah, sometimes… I think it depends, so it’s like, if I’m writing something really hefty, then yes. I try…
126 00:20:06.360 ⇒ 00:20:18.530 Shreya Chowdhury: every once in a while, I try to do it myself, just so I don’t get rusty, because I did notice that a lot of times when I’m using it a lot, it would kind of be like, okay, like, let me not forget the fundamentals, …
127 00:20:18.910 ⇒ 00:20:37.199 Shreya Chowdhury: But, yeah, like, for a lot of stuff, like, I would use it, or, like, even if I wrote it myself, I would plug it back in to be like, is this the most efficient way to write this, or is this the best way to write this? I think it’s really good for, like, also documenting the code, so it’s like, when we build data pipelines, it’s like.
128 00:20:38.040 ⇒ 00:20:50.260 Shreya Chowdhury: if I’m not owning that space anymore later, I want the next person to be able to come in and, like, know exactly what I did and why I did it that way, and then they can build off of that, so even if I wrote it myself.
129 00:20:50.390 ⇒ 00:20:59.059 Shreya Chowdhury: Or, like, if it was writing it for me, I would tell it, like, oh, make sure, like, for each CTE, like, you explain, like, the logic and, like, why you did what you did.
130 00:20:59.530 ⇒ 00:21:00.689 Shreya Chowdhury: It was really good. Awesome.
131 00:21:01.290 ⇒ 00:21:05.990 Amber Lin: Yeah. … And you’re looking for something full-time, right?
132 00:21:06.490 ⇒ 00:21:24.679 Shreya Chowdhury: I’m… yeah, ideally I’m open to full-time, but I don’t know what you guys are looking for right now. I think, like, when first messaged me, he said he’s looking for people in part-time and in full-time roles, but I was going to ask, like, what you guys are looking for specifically.
133 00:21:24.680 ⇒ 00:21:34.770 Amber Lin: We… I think we would love to have you as full-time if we have all the… if we have enough work. So I think what we…
134 00:21:35.030 ⇒ 00:21:36.719 Amber Lin: would start the…
135 00:21:36.860 ⇒ 00:21:55.259 Amber Lin: So the process usually goes, we have about 2 weeks or so of a trial period, a paid trial period, and same rate that will get negotiated, not with me, but maybe with Robert. And after that, we decide, oh, do we want to continue to have a longer contract together? And then, probably it would be part-time, but I think
136 00:21:55.360 ⇒ 00:21:58.630 Amber Lin: They would want to see if they can
137 00:21:58.840 ⇒ 00:22:10.649 Amber Lin: turn it from an hourly contract into a full-time salary contract. I think that’s… that’s what happened for me, and it happened for, all of the new hires ever since I got on board.
138 00:22:11.370 ⇒ 00:22:19.469 Shreya Chowdhury: Okay, sounds good. So, for the first two weeks, it’s a trial period, and it’s…
139 00:22:20.140 ⇒ 00:22:22.779 Shreya Chowdhury: Part-time trial period, not full-time, is that correct?
140 00:22:23.760 ⇒ 00:22:25.110 Shreya Chowdhury: Okay, sounds good.
141 00:22:25.320 ⇒ 00:22:25.850 Amber Lin: Yeah.
142 00:22:27.230 ⇒ 00:22:35.070 Shreya Chowdhury: And then… If the contract is extended, is it salaried, or is it still hourly?
143 00:22:35.540 ⇒ 00:22:39.630 Amber Lin: I believe that’s something you can negotiate with them, but….
144 00:22:39.630 ⇒ 00:22:40.000 Shreya Chowdhury: Okay.
145 00:22:40.000 ⇒ 00:22:42.740 Amber Lin: Sounds like right now, they don’t have…
146 00:22:43.320 ⇒ 00:22:59.470 Amber Lin: they don’t have that much work to have it as full-time. It might change in a month or two, because we… our clients are… I think the sales pipeline is working, there’s not new clients that’s signing right now, and so…
147 00:22:59.700 ⇒ 00:23:09.640 Amber Lin: I think you can check in with Robert to see, okay, what is the timeline like? You should tell him that, hey, I want a full time, and I think he’ll try his best to give you a timeline.
148 00:23:09.810 ⇒ 00:23:15.859 Shreya Chowdhury: Okay, yeah, that sounds good. But yeah, I think to start with, like, a two-week trial period sounds great, yeah.
149 00:23:15.860 ⇒ 00:23:16.900 Amber Lin: Yep, good.
150 00:23:17.210 ⇒ 00:23:25.250 Amber Lin: That’s all the questions on my end. I hope I answered any of your questions. If anything else, let me know and I can answer them.
151 00:23:25.480 ⇒ 00:23:30.199 Shreya Chowdhury: Yeah, no, I think you did a great job of answering all of my questions, too. …
152 00:23:30.520 ⇒ 00:23:37.210 Shreya Chowdhury: Yeah, like, I’m excited to learn more about the company and, like, the internal tools and the onboarding and stuff like that.
153 00:23:37.340 ⇒ 00:23:40.389 Amber Lin: Awesome. Are you talking to Awash this week?
154 00:23:41.250 ⇒ 00:23:42.640 Shreya Chowdhury: Talking to who?
155 00:23:42.810 ⇒ 00:23:57.050 Amber Lin: I know… I think Rico also asked you to schedule… I’m not sure if you should schedule with me, or also you should schedule with a wish, but it’s whatever they said on the email. If you don’t see it, then it’s… it’s okay.
156 00:23:57.170 ⇒ 00:24:14.450 Shreya Chowdhury: Yeah, I don’t think I have anything yet, but I do have another call with Utham tomorrow, to check in, so maybe it would be following that one, but otherwise, I don’t know if I’m, like, I should expect to see one sooner. Feel free to send it over to my email, and I can take a look.
157 00:24:14.450 ⇒ 00:24:17.260 Amber Lin: Okay, sounds good. Oh, what time zone are you based in?
158 00:24:17.510 ⇒ 00:24:19.340 Shreya Chowdhury: I’m Pacific time zone.
159 00:24:19.340 ⇒ 00:24:21.679 Amber Lin: Oh, awesome. I’m based in LA.
160 00:24:21.680 ⇒ 00:24:22.810 Shreya Chowdhury: Okay, nice.
161 00:24:22.810 ⇒ 00:24:30.529 Amber Lin: Okay, alright. Let me know how it goes tomorrow when we talk to Utam, and I’ll show Utam, and I’ll tell Robert and Utam how this call went.
162 00:24:30.530 ⇒ 00:24:33.069 Shreya Chowdhury: Okay, cool, sounds great. It was really nice meeting you.
163 00:24:33.070 ⇒ 00:24:35.110 Amber Lin: Yeah, you too. Awesome. Have a good one.
164 00:24:35.890 ⇒ 00:24:36.740 Amber Lin: Bye.