How to Have a Career in Data Science in 2025 with Dawn Choo, Data Careers Influencer, Co-Founder at Interview Master

Dawn Choo: Yeah, I think it's affected in a few ways. The first is obviously in the day-to-day workflows, right? There is a requirement almost, or yeah, I would say a requirement for every data scientist to be able to incorporate AI into their workflows one way or another.

You don't have to, so it's not like a hard requirement, right? No one's gonna tell you, Hey, use ai, but. If you don't, you end up falling behind. So that's like the unwritten role of how gen AI fits into the data science world right now. Everyone in the day-to-day jobs are using AI to make themselves more efficient.

The other way Gen AI is impacting the data science role is that we're starting to see a lot more roles open up. In the AI space, like the traditional data science role, but in the AI space, right? So you could be a product data scientist. So that's what I did at Meta in the past. You could be a product data scientist, but the product you work on is AI itself or an AI application.

So maybe you work at Open AI or philanthropic, or you work at a company trying to incorporate. Those AI products into their own product. And then the third piece is of course, these ai ML engineering roles. We've seen that evolve a lot over the years. For a long time it was very ML heavy.

You're the person who's building the. ML models and putting them into production. And then now we're just seeing that shift over into the AI space. So learning how to implement AI models, train AI models for your company's use case. So I would say that's probably the three ways I see it impacting the data science job.

It obviously changes a lot. Month to month, even week to week. So who knows what it'll be like in a year. 

Richie Cotton: That's pretty cool. So I like that. So you can either use AI to make yourself go faster or use data science in an AI application context. Or you can just I guess the machine learning stuff where it's I was just writing psychic learn code.

Now I'm doing something with fancier AI models. Maybe. We'll take that first one first then. So talk me through what are the ways you can use generative AI to make yourself go faster as a data scientist? 

Dawn Choo: Okay. So the ways I use it, I'm sure there are even more ways. I feel like every few months I, there's a paradigm shift in the way we work right now.

I use it a lot to write basic code. So stuff that like used to take me a long time to write, I can get AI to do it for me. So I'm a huge fan of Cursor. I've been doing all my Python analysis and cursor now even, it's a little bit clunky, but one of my favorite things is. To tell it to write me Python code for generating charts.

'cause I never figured out charts, charts with me is it's never worth the effort. I just take the data, I copy and paste into Excel or Google Sheets and then make my charts there. 'cause I know I can make it pretty then. But now with ai, I just say to. Clots probably my favorite sonnets, probably my favorite model right now.

So I'll say two clots on it. Hey, make me this chart. I want it in a two by two matrix chart. One looks like this, chart two looks like this, chart three looks like this chart, four looks like this. I specify the colors, I specify the titles, the formatting, everything. And I've got just click generate. I go grab a sip of water and within a few seconds I have code.

That's the first use case for me coding, especially for things that I've never really enjoyed doing. The second piece is around the communication. A lot of times a data science to a data scientist spends a lot of time writing documentation, right? So there's like documenting what you've done and then also.

Writing out the implications of what your work means for the business. That takes a ton of time and it still does honestly, with ai 'cause that's the value add you have as a data scientist. So you still keep doing that work, but I found I can give it a bunch of gibberish, almost like a stream of consciousness writing.

And I just say, clean this up for me. That's a good use case I think for Chad, GPT. So I would just say. I literally would just close my eyes and take as fast if I can anything. I'm like, that comes to mind. And then I copy and paste that to chat GPT and say, here's a gibberish of my thoughts.

Clean it up for me. Keeping my sentence structure, keeping the thoughts as they are, just polish it up. And she does a really good job with that. That has made me a lot more efficient because I don't have to like polish as I'm writing. I just. Dump my thoughts and then have Che GBT basically clean it up for me.

And then there's also obviously a lot more like context gathering that you can do with ai, especially with the deep research functionality that like, I can't remember who started deep research.

Richie Cotton: There's a few of them around now. Yeah. 

Dawn Choo: Yeah. But every company has one now. So using deep research has been really interesting.

So in the past, if I wanted to know what is the benchmark for open rates for a marketing email, for an e-commerce company, I usually to have to go, Google it and then read a few articles, decide which ones I trust, and then compile. A reach now I can go to Chad, GPT or Claude or Grok or whatever and say, here's my question, blink on it.

This usually takes a little bit more time, let them think on it and they come back with an 18 page document that you can read with everything summarized, all your sources cited, and so really saves a lot of time there too. 

Richie Cotton: Okay, nice. All great use cases there. Certainly. Like you mentioned like writing code and there are definitely some bits of code I enjoy writing, but other bits you're like, that's tedious and.

Being able to outsource that technology also is very useful just for your mental health e even if you don't care about the productivity gains. I also like the idea of cleaning up gibberish thoughts. As a podcaster, I have plenty of gibberish thoughts. Try and convert them into something sensible then yeah, that seems incredibly useful as well.

Dawn Choo: What has been, can I give you some, a tip that has been really nice for me too. I've started using Super Whisper. Have you used it? I 

Richie Cotton: don't know. Super whispers. Tell me about it. Okay. 

Dawn Choo: I think it's also some kind of AI company, but what they do is they basically take your voice and they transcribe it for you, and they do it with quite good accuracy.

So like text to speech on an iPhone or something. I basically have the same thing on a computer, which makes like dumping out thoughts even easier. So now I don't even have to type, I just speak my thoughts. So it saves even one more step. 

Richie Cotton: Okay. I like that. Not even bothering typing, just.

I ramble a bit, and then suddenly that's gonna turn into some kind of coherent transcription and hopefully a nice report. Okay. That seems like a useful, some fairly simple workflow improvement then that you can use technology for. Do you wanna talk me about through the second thing you mentioned before about how data scientists are now taking part in like creating data products or AI products, things like that.

Yeah. Talk me through, what does that involve? 

Dawn Choo: I personally haven't worked in this space, but I have a few. Clients and friends, ex-coworkers who've moved out from a traditional product space. So let me just define what traditional product space is. So let's say I used to work at Instagram and I worked on stories.

I didn't, but just as an example Instagram stories would be like a more traditional, maybe consumer phrasing product would be a better way to think about it. A lot of people have now moved over to companies like OpenAI, anthropic perplexity, and now the product that they're working with is not.

Instagram stories. It could be like perplexity search, for example. So you're using the same techniques in the same way of solving business problems, but now you're applying it to an AI space. Some examples of companies that are not like. AI companies, but our tech companies implementing ai, I think have done it really well.

Notion is one, Zapier is another companies that like have integrated AI into their space, and then of course they have product data scientists or just product analysts, data scientists, et cetera. Working on those products to make it better. 

Richie Cotton: Okay. Yeah. If you're building like a product, whether it's software or something else, then you really are gonna need data scientists to say which features should we have?

And, which one should we not? And what works and what doesn't. 

Dawn Choo: Yeah. Yeah. A lot of just are we doing well? Is this really what we should be doing? What should we do next? Those kind of strategic business props. 

Richie Cotton: Okay. Yeah. So I guess the stuff we were talking about before, like competitive advantage, like using AI to make you go faster.

This is about making sure that you are doing the right thing, so going in the right direction as well. I'd talk a bit about SQL because you are at least from your social feeds, you are one of the most enthusiastic people about sequel that I think I've come across. 

Dawn Choo: Oh, I'll take that.

Richie Cotton: Okay.

So tell me what's your favorite bit of SQL syntax? Is there something that you think more people should know about? 

Dawn Choo: I like window functions a lot. The reason I say that with a few things, a few reasons from my past. One, I used to switch between SQL and Python because I would be able to do a lot more complex processing and Python, and then I switch it back to Excel or Google Sheets or something else.

Like I, I just move my data between the different tools. Based on what is most effective for what I need. And then when I found window functions, I realized a lot of the processing, the more complex processing that I'm doing in Python, maybe I'm doing a loop instead of having to do a loop in Python, I can do that directly in SQL with a window function where it makes it really efficient.

'cause SQL is very good for hitting large databases and processing large amounts of data. So I can take a lot of the processing, I'll do downstream in Python and move it upstream into sql. Do that with window functions. So that's my first window functions. Story or reason why I love sql. And the second was I had this interview at Google I wanna say 20, 22, 23, sometime around then I had a Seql interview for my first round.

And that point I was pretty cocky about how well I knew sql. I think I really practiced for the interview. I went in there, got a question about window functions, could not do it. I actually got lucky 'cause the interviewer let me switch to Python, so I just did it in Python. 'cause I could do it in Python, but then it made me realize hey, I think I'm missing out a big section of what SQL could do if I understood window functions.

And so I've gone a lot deeper into window functions and it really does make a huge difference in terms of like how many lines of query ends up being, for example, how many times you switch between Python and SQL window functions, I think is one of the more powerful things you can do within sql.

Richie Cotton: Absolutely. Yeah, it's one of those really important things that not many people know about, I'd say. It's one of those things I only use occasionally, and I do have to look up the syntax every time. 'cause it's a remarkably complex syntax compared to it in Python. Okay. Yeah. So window function's horrible syntax, but very much worth knowing about.

Okay. Cool. SQL's been around a while, so last year we had Don Chamberlain on the show. Talking about like 50 years since seq, since he created sql. But is anything changing? Maybe not so much on the SQL syntax front, but other things happening at the database level, but worth knowing about?

Dawn Choo: Yeah, I don't think SQL has changed very much, but I will watch the episode of the Creator sql. I missed his name, but I will, I'll watch that so I can get his perspective. What's changed in sql? I think there are a lot more tools that have been built that are wrapped around sql. Maybe wrap is not the right word, but tools like DVT, airflow, basically these products that help you build out a database and your data pipelines really easily.

A lot of these have been gaining a lot of popularity. I'm sure they've been around for a long time, but gaining a lot of popularity in the last. Five to 10 years. And so some of the data science job has kind shifted over to being able to do more ETL type work because now it's a lot easier for anyone to do it, right?

So as a data scientist in the past, maybe only a consumer of data, now there is an expectation because of these tools and how it's allowed us to make. SQL and ETL pipelining a lot easier. A lot of the data science job has shifted further upstream into the data pipeline database creation piece.

Richie Cotton: Oh. So this is interesting. So because I think when the data scientist role first started out, it was just like, we actually need to hire six different people with different skins, like statistics, programming and and all the rest. But we can only afford one person is a very general role and it seems like it's becoming slightly more specialized in different areas then so can talk through.

The flow then. 'cause you've got like data engineers, you've got analytics engineers, you've got like data analysts and business intelligence analysts and all that as well as the data scientist role. So yeah, talk me through how all these roles differ. 

Dawn Choo: Oh, I'm scratching my head a little bit because I feel like they are different, but they're also the same in a lot of ways.

As one data professional, you should be able to do. If you think about a funnel, which we'll talk about in a second, the expectations a data professional is you should be able to do your job one step above the funnel and one step to below the funnel. Obviously that differs where depending on what company you're at, what industry you're at, or even like where you are in your career.

But generally the way I think about it is at the very. Most fundamental layer. We have your data platform engineers or data engineers. I think a lot of times they're use interchangeably. These are the people who are writing the code that builds out your data ecosystem. Then we one step up from that, so people who build on that data engineering work.

We have your analytics engineers, analytics, engineering. I think it's a very new role. I've only really started seeing pop, seeing it pop up the last two to three years. But they would, I would say they're like data engineers, but with product and business knowledge. So the, what they do is they're very familiar with the databases and the data pipelining, but they also incorporate the business logic.

To basically make the data that you have more easily accessible and more easily usable. So I'll give you a specific example 'cause I think that was vague. Maybe we have in our pipeline something like every single login attempt that a person has ever made. On, let's say Amazon. Okay, so we're gonna go Amazon.

So now your analytics engineer might say, okay, that's gonna give us billions of rows. That is not going to be accessible to an analyst or data scientist to do their analysis. So let's take that data and aggregate it into forms that a data scientist or a data analyst might be able to use. So instead, they might create these aggregation layers, which are.

Say for this user ID on this day, they attempted to log in five times or stereotypes or one time. So now you can imagine the table goes from like billions of rows down to something like a few million, which then becomes a lot more usable. So there're very much the creators of the analytics layer of your data.

So from an analytics engineer, one layer down for that, I. I think it roughly branches into three roles. So these are, now you're starting to look at consumers of data, right? So you have your machine learning and machine learning engineers. They're taking the aggregated data, some level of aggregation of the data.

And they're building U ML models and putting those in production. You have your data scientists, in this case, maybe like a product data scientist who is making sense of the data, all your consumer behavior data, your historicals, and coming up with. Strategic recommendations for the business. So in this example that I gave, for example, they might say something like, users who log in more than five times or attempt to log in more than five times are probably a bot.

We might wanna consider removing them from the system of blocking them. And then the third layer would be your data analysts, your business analysts. These folks, honestly, again, I feel like the data scientist, data analyst roles can sometimes merge together and seem very similar. But your data analysts might be more focused on, say, building out reports or building out dashboards.

So your stakeholders, your non-tech and your tech stakeholders can easily access the data without having to write any queries. But yeah, roughly, that's how I think about the system of data flow across the different roles. 

Richie Cotton: Okay. Yeah. Yeah, that's interesting. So you got like the I'm taking data from absolutely anywhere, data engineer and shoving it into your data warehouse and then the.

Analytics engineers next. Providing some kind of semantics on top of that, then data analyst or data scientist goes and analyzes it and then you got some sort of business person consuming the result. That sounds about right. Let's talk about what the hiring process. So if you are a hiring manager or if you're looking for a job as a data scientist jump me through, first of all, like what sort of qualifications are generally desired for this?

Dawn Choo: So there is tech experience and then there's like soft skills experience the way I generally would think about them. On the technical side, we have your coding languages the most common SQL and Python. Python and R can use interchangeably. To be honest, I see Python used way more in the industry than R.

My recommendation, if you're. Learn Python r just pick one and pick Python. 

Richie Cotton: I would say no, this makes me very sad 'cause I came from the R community. It is yeah, it's just dying out when it's pythons ticking over everything. I'm okay with Python's a nice language. Sorry.

Carry on. 

Dawn Choo: Okay. It's easy to switch between R and Python, so if that makes me feel better. Okay, so tech skills swipes. We have Python and we have sql. Then we have your foundational understanding of statistics, right? So that's understanding everything from probability to basic machine learning models.

Of course, if you choose to specialize in machine learning, engineering. You gotta go past the basics. But for most data science roles, I think basic machine learning is good enough. Regression clustering and decision trees, those three, I would say are the ones that you need to know. Then there's experimentation.

So AB testing how does that work in the real world, right? So we all know you do a, you run the testing control groups, you do a t-test, you look at the P values, but understanding the nuances and how actually an AB test works in the real world, I would say those are the three. Buckets of tech skills that you need.

Then on the soft skill side, the product sense and the business sense piece is probably going to be the most important. To your point earlier, we are building all of this data and make, doing all of these analysis so that someone on the business side can use it and consume it. So being able to see things from the perspective, let's say a product manager or a business owner, a marketing manager, a sales lead.

Is really important so you can make sure you're doing the right thing for your stakeholders and for the business. And then the other very important skill on the soft skill side is communication. I think people don't always recognize this, but communication is explicitly a criteria that you're graded on.

So being able to communicate well is as important as being able to write good sequel. And if you fail at communication. You are probably not going to get the gel. So communication and product sense, I would say is the two big soft skill pieces that they look for. And then of course, be a good person.

Everyone wants to work with a nice person. 

Richie Cotton: I like that. I think that last one is something that is often forgotten about is be a good person. Yeah. 

Dawn Choo: You forget how much people like you when you're nice.

Richie Cotton: Yeah. Definitely an underrated skill there. Wonderful. Alright suppose you have all these skills.

How do you demonstrate them to the hiring manager? What kind of, qualifications you need? Or what do you need to do to show that you have these skills? 

Dawn Choo: Yeah. It really depends on where you are in your career, right? When you're, let's just break it down. Let simplify to being early career in the mid and beyond career in your early career.

So let's say you're transitioning into your first data science role, or you are just outta school and looking for your first data science role, typically. You want to be able to demonstrate the soft skills in projects or past professional experience, having done those things in your projects and past experiences, right?

So being able to communicate well. It could be you did a custom project for school and here is your final deliverable. Or if you are working in a business, maybe you might not have. A technical person, let's say you're a salesperson, right? Being able to show, Hey, I was able to understand the business and drive business impact, even if it wasn't like a data skill for the tech skill side, this is where your portfolio becomes really important.

Your portfolio basically would be a repository of all your projects presented really nicely, demonstrating what you did. What your recommendations are, and if any, what? What's the impact? So what could go into your portfolio, assuming again, this is your early career person. It would be maybe your school projects that you have done and any extra projects you've done, like hackathons or just you find dataset on and you start doing it.

All of those would go into your portfolio. So for only career people, finding as much experience as you can within your school experience, your current professional experience, and building out a portfolio. For mid to late career folks, you are. You don't need a portfolio. Your entire portfolio is your professional experience, so what you want to be able to do is demonstrate you're able to drive impact or you have driven impact in your past experiences when impact.

I think about it in three categories, and this is how we did it in meta. There is strategic impact. So can you convince your business partners to change direction of the business, invest in a new area put something new on the roadmap. There's operational impact. So can you convince again, your business partners to run an experiment or remove.

A feature that is broken or something to handle, like smaller and shorter term than a strategic impact piece. And then you have your data infrastructure impact. So how do you take the data literacy and the data availability for the company and raise that? So that's things like building out an experimentation platform or improving the experimentation platform, building out new metrics, building out new alerts.

Basically everything, what you mentioned data engineer would do or analytics engineer would do. Contributing towards that further up funnel pieces that we talked about earlier. But yeah, it's, if you're in your late career knowing a portfolio, the key is to really be able to draw out the impact of what you've done.

Make it quantifiable to a business impact and then being able to communicate that through your resume and your interviews. 

Richie Cotton: Interesting. Okay, so I love the idea of like really focusing on the portfolio, particularly at the early career stage and just saying, Hey look here's some proof. I can actually write this code.

I can crunch the numbers. I can get to that business insight. It sounds like later stage create, it's actually slightly harder to demonstrate then, 'cause I guess a lot of the stuff went well. I had an impact. That's very difficult to prove in a public way rather than just oh, I have these conversations and you can see it in the numbers and you're gonna have to trust a bit.

Is there a way to. Demonstrate this for real, 

Dawn Choo: for impact, for mid tolay career people, I feel like it's actually easier because you have more opportunity to actually drive impact and then measure the outcomes, right? The, I would say the outcome measurement doesn't have to be so precise. You don't have to be like, I increase revenue by 13.47% with a P value of 0.02.

It doesn't have to be so specific, it can be more an estimation of what you're able to do. When I think about impact for Min, actually anyone really, okay. This is, can be used for anyone. So when you think about impact, I think there are two ways to do it. The first, which is my preferred way, which is to measure impact on business outcomes.

So business outcomes would be like revenue, increased revenue. Decrease cost, increased conversion rates increased click-through rates, basically any kind of business outcome that the business cares about. The second option, which is less desirable, but also a good option if you cannot quantify impact, it's to quantify scope.

So right, instead of doing an output quantification, you do an input quantification. So that is things like, I helped manage analytics for a $10 billion. That's a lot. $10 million business, or I worked with 135 data sources and aggregated it, right? So being able to quantify how much work you did versus like how much impact you drove.

But the further you are in your career, the more important it is to focus on option one, which is the actual business. 

Richie Cotton: Outcomes. So actually these things have they're useful not just for hiring, but also for things like your annual performance review. Even just anytime you speak to your boss or another manager, you're like, oh, hey, this is the stuff I've been doing.

This is the stuff we've been working on. This is how I've had an impact, and that seems like it's gonna go down pretty well. 

Dawn Choo: That's actually very good advice. Yes, I would say the, whenever you're working. Have a list, and I always have a doc. I call it like Dawn's wins, and I just throw in every single thing that I have done, every compliment I've gotten.

I will take screenshots of those and every measurable outcome that I've driven in that doc, because every six months or a year, whenever you have to do your performance reviews, that is going to be all the evidence of the work you have done. And to your point, that also can then go into your resume when you're ready to look for your next job.

And also, if you ever feel sad or down or imposter syndrome, you can just go back to that doc and just remind yourself, okay. I'm pretty good. 

Richie Cotton: I do love that idea of just like keeping a list of all the cool things you've done and all the sort of comments you've received. I wish you'd had this conversation a few weeks ago.

Just, I was doing my before self-reflection was like, I dunno I think I have quite good, I must have done something in the last six months. But, so yeah. That seems like a useful thing to do write stuff down rather than trying to remember what you did. Six months previously, we were talking before about the hiring, getting hired or hiring people for data science roles talk me through what's the hiring process typically involve? 

Dawn Choo: Oh, yes. Very involved process for data science roles generally. So typically it starts with a recruiter round. So you talk through a real let's start even for that funnel. So it starts with applying for the job.

At this stage I recommend trying to get a referral if you can. So if y'all are not familiar with referrals are pretty much someone at the company will vouch for you and send your resume directly to the hiring manager or to a recruiter, right? So at the application stage you should try and get referrals.

If not, I would just apply directly on LinkedIn. So once you get past that, I honestly don't know what goes on behind the scenes, but my understanding is there is some kind of automated system. Reviews your resume, and then a shorter list goes to a recruiter and a hiring manager. They decide who they want to talk to.

Then this is where your first interview round starts. You talk typically to a recruiter first. So these calls very low pressure. Usually a high level overview of what you have done in your past, why you're interested in this role. The recruiter must to make sure that you're a good fit before they de they pass your resume over to a hiring manager.

At that point, your hiring manager or the hiring manager would say, okay, I wanna. Talk to them. They're a good fit, they're interesting. Then you go into, typically a cat screen round. Sometimes these are automated through platforms like Hacker Rank, I think do them like, almost like a test, a SQL test, a Python test.

I've actually never done those in my career. I don't know how. I've interviewed probably at 40 to 50 companies in my career. I've never had a test. But the first round typically would be some kind of tech screen. So for most companies it would be a combination of sql, Python. And product or business case study.

So the most important skills to have, sometimes they'll throw in a little bit behavioral, sometimes they won't. Then after the texting round, they typically will move you on to a hiring manager or an onsite round. So this is the full loop of the most stressful interviews you ever had in your life.

No, I'm just kidding. It's not always so stressful, but typically you'll be like four to six rounds of just different types of entities. This is what you have your behavioral interview, a coding interview, SQL, Python typically, and then you have a few case studies, and then sometimes you'll have a product partnership.

Behavioral and abuse. So let's say I'm working on a pro I'm working for a sales data science role. Then I might talk to sales lead, for example, in that realm. Sometimes they have take home assignments, right? So they give you a very intensive case study. They say you're 24 to seven, two hours to finish it.

And it's your term, your time to basically show off everything you can do. It's typically a case study that you do on the jump. You wanna show basically your entire process without having to talk through it. So very good documentation. Okay, so that was the take home. Then you go on the full loop and then now what I've seen more and more commonly at these.

Culture fit rounds. I don't know how I feel about them, but I've done a lot of them. But they're basically rounds where you interview with very senior people at a company or people you would never work with. What they want to do is evaluate whether or not you are a good culture fit, which I don't like those interviews because I feel like culture fit is such a vague term and I cannot prepare for it.

It's hard, I think, to protect against biases in the hiring process when it is so nebulous. But you would just talk to a person and they'll ask you some random questions that like, you're like, why does this have anything to do with my role? I kept trying to think about what some of the questions I've gotten, someone was like, what is something you've learned recently not related to data?

And I was like, 

Richie Cotton: Oh, those questions are difficult. I'm like, I'm sure I learned something. I probably listened to a podcast. They were saying some things. I'm not sure. Yeah. Tho those questions are hard. So probably best to prepare for that sort of thing in advance. I guess there's probably a way to get like list of culture fit questions and go through least preparing some answers for those.

Dawn Choo: Yeah, and I think it's okay to also just be like, okay, I can't think of anything. Give me a minute. Then use like some silent times. So just rack your brains. I can't remember what I came up with, but I'm pretty sure it was something ridiculous. 

Richie Cotton: Yeah, I'm sure. Just try and recall some factoid recently.

I dunno whether that's gonna help you get the job or not. Yeah, probably don't into that advice. That's interesting. It's, I think you've been in a place in which you have pretty intense processes. I do a lot of interviews. Let's see there. I think it's a vary quite a lot from company to company, but certainly there's gonna be some kind of screening interview and then a technical interview and maybe a take home case study as well.

So yeah, there's gonna be a few rounds like that. Okay. And suppose you get the job how is the sort of. How are you, how is your performance measured over the first few weeks in a role as a data scientist? Like how do you know whether you're doing a good job off, off the bat? 

Dawn Choo: The best way to know if you're doing a good job is to ask, I would say just be very explicit asking for feedback.

So what I have seen in the companies that have the best onboarding process is you have a 30, 60, and 90 day. Checkpoint that would say by 30 days you would have delivered this analysis and talked to December stakeholders by 90 days, for example, you are delivering on your first analysis, then we're not gonna tell you what it is at this point.

You should be independent enough to figure out where you can drive the most impact. I would say if you going to a new job, if you don't have one of those 30, 60, 90 day plans, get 30 minutes to your manager or 60 minutes to your manager. Sit down and write those plans out because you want to have a very clear focus, your first 30, 60, 90 days.

And in terms of how would you know if you're doing well, you have to ask, and this is so important getting feedback if feedback's not automatically provided to you, which any the manager should know to give you feedback whether or not you want it. But if your manager is not doing that, ask for it.

In fact, just ask all the people who you work with very closely. So you have an onboarding buddy, like maybe any another data scientist who is helping you onboard. You can get feedback from them. You have a product designer, product manager that you work with closely. Get feedback from them too. And you can be like, it doesn't have to be high pressure.

You don't have to write anything down. I just wanna talk to you, just see where I'm at see how I can be more impactful. I think it's helpful to anchor on getting some growth feedback. 'cause I think a lot of people tend to be very positive, right? So you can, they'll tell you all the good things you're doing, but you're like, that's not what I want.

I want, how can I be better? But yeah, just ask for the feedback. If you're working for a bigger company, a lot of times too, they have. Written out guidelines on what, how you should be performing at different levels. So if you're a junior data scientist versus a mid-level versus a senior, versus a staff versus a principal, here are all the things that you need to be doing for each of those levels.

I will go in and be self-critical or even have them conversation with your manager with that rubric and say, tell me how I'm doing against each of these things. Or if you're very ambitious, say. Tell me how I'm doing against each of these things at the next level, because I wanna get promoted. So if I'm already senior, how am I doing against these staff requirements? 'cause I, I want to get there. 

Richie Cotton: Really the secret is communication. We know you have the communication skills. 'cause that was one of the things you said you had when you were getting hired. And obviously you got hired, so you must have them. Okay.

So yeah, talk to your boss and that's probably a brief, useful thing to do. I'd like to talk a bit about keeping your skills up to obviously particularly the last couple years, the AI tech stack's been changing like every couple of weeks. Data science stack is moving slightly slow, but still changing.

How do you keep your skills up to date? 

Dawn Choo: Okay, so I think there are two pieces to this. The first is the foundational skills haven't actually changed, so make sure you're keeping your foundational skills. What are foundational skills? Your basic coding language, right? SQL and Python. Those haven't changed.

And then statistics, so basic probability, basic machine learning models. Those haven't changed. Like you need to know those foundations because, huh? Who knows where the world is going, but at least for now, the AI wave hasn't changed those foundations, and those are still extremely important to landing a job and then being successful at your job.

In terms of upskilling with all the new tools and everything coming your way, I would say one, you cannot shy away from them. I think sometimes I do this when they're, especially when the AI wave first started, I was so scared of what. What's going to happen and how much was gonna change it like almost made me hate AI right away.

But then I realized okay, that's like a wave that we gotta just go head first into. So you gotta recognize that like this change is happening, you have to adapt to it. Then pick one tool or one part of this wave that you're most interested in and go. Deep into learning about it. So for me, I've personally looked into building AI products.

I've actually gone so far into it. I quit my full-time job to go build AI products full-time. Pick one thing that you're really excited about and go deep into figuring out what it is. It could be computer vision, it could be application of AI in consumer products, right? It could be really anything that you find interesting.

Pick one thing, stay focused, and go and do it. The other thing I try to do, I think a lot of companies. Have good intention to do this. I dunno how well it's executed, but a lot of companies try to say 10 to 20% of your time, your actual day job should be about learning. Pick one of the new skills or tools that are out there and use that for your 10 to 20% of the time.

Make it part of your workflow. Make it part of your work goals. And so it's like forces you to really get in there and learn them. But I'd say like the world's changing so fast. Just stay adaptable, stay up to date with the news, and recognize that whatever you're learning now could be obsolete in a year.

And that's fine. 

Richie Cotton: Okay. Yeah. Certainly I think it's good advice that you can't possibly keep up to date with every single new tool that's coming out, but just spend. 10 to 20% of your work week or whatever. You spend a few hours each week trying to keep your skills up to date and then just pick something interesting that's gonna help you in your job at some point, except that sometimes, maybe it doesn't work out.

Just to finish up, what are you most excited about in the world of data and ai? 

Dawn Choo: Ooh, what am I most excited about? 

Richie Cotton: I know there's a lot to choose from.

Dawn Choo: Okay I'll give my two thoughts. The first thing I'm really excited about is I'm starting to see some tech companies really implement AI very well into their products in a way that makes live a lot easier.

So I just started using Notions, AI and I, my mind is blown, like they figure out how to search basically across Google Drive, Gmail, slack, and all of your notion docs and can do research across all of that and summarize it into something helpful. And I just love seeing companies be able to implement AI in a way that is truly useful.

I think a lot of companies want to implement ai, but they work backwards from. Doing AI for the sake of doing it versus a natural user problem, but I'm starting to see a few companies really come up and do very interesting things with ai and I'm very excited to be a consumer in this case. I can give you the example I did yesterday.

So I do a lot of mock interviews with clients basically to help them get jobs. And yesterday just outta curiosity, I was like, Hey, look through all my mock interview feedback. 'cause I use notion. And tell me what are the 10 most common mistakes that people make in interviews. It like just went through and I was like, oh, this is so good.

This is the feedback that I write to almost every client that I talk to, and it was just like summarized so nicely and eloquently for me. So that's the first thing I'm really excited about. The second piece is. There are certain things because now I'm building my own business and my own product primarily through ai.

There are some parts of it that have been really challenging as someone who's like trying to bootstrap the whole thing, I'm not paying for a designer, right? I'm just gonna do it all myself. I'm very interested in a world where we can a. Design to AI or like outsource design to AI and what that would look like, because I think some companies are doing that pretty well.

V zero dev has been pretty good and just claw in their regular, like clot AI interface has been pretty good. I'm very interested to see where that goes. 

Richie Cotton: Okay. Yeah, so I love it when, aI just works and you have this little magic moment and it's actually helpful because there are definitely some products where it's like someone stuffed some AI in there and it's more of a hindrance than a help.

Yeah. So I like it when it does work. 

Dawn Choo: Same. I'm like, sometimes you're like, what? Why did you, did this need to be ai? This could have been, it could be a deterministic scorecard, not even a model, just. It could just be deterministic. 

Richie Cotton: Absolutely. But yeah. The noting example is particularly interesting 'cause it sounds like you weren't even quite sure what the thing was you wanted to automate.

You was like what am I doing? Or what's the feedback I give most commonly? So it was like. The AI helped you find out what the thing was you needed to automate which bits of feedback you need to automate. So that seems pretty cool. Finally, I always want recommendations for people to follow.

So whose work are you most excited about at the moment? 

Dawn Choo: Wait. Okay. I have another one for the last question. Can I go back and say one more thing? 

Richie Cotton: Tell, tell me one more thing. 

Dawn Choo: Okay. The third thing which I was, I just thought about, and I don't know, I don't know enough. To really talk about this in detail, I am very curious about a world where you can train your own AI models for cheap.

Right now it costs a lot. Not right now, let's say a few months ago, even a year ago, it costs tens of thousands of dollars, even more than that to train your own AI model. But there are recent projects I've seen that has taken it down to a few hundred dollars, even like a couple thousand dollars for a small scale.

Trained AI model, or it's called small scale model, trained on your own data. And I think that's very interesting and a lot of use cases for it. So that's another thing I'll be, I need to learn a lot more, but we'll definitely be keeping my eye. 

Richie Cotton: Definitely. While the sort of cutting edge foundation models are outta the price range of most individuals, like you don't have billions of dollars to spend the idea of having a small model that is tailored to your specific use case, that's very useful for a lot of businesses.

Possibly even individuals in the future. Yeah. Yeah. That's something cool to look forward to. So yeah, finally just to wrap up I always want more people to follow. So who's work are you most excited about at the moment 

Dawn Choo: on the AI space? I'm very curious or interested. So the design automation design fuzzy AI automation thing.

I think his name is Peters Guano. Maybe I might be ptro. Okay, I'll send you the actual name after this. I think he's building something. I'm very curious to see what comes up there. And then do you wanna hear about creators or like. Products. 

Richie Cotton: Cool. Anyone saying cool things? 

Dawn Choo: Anyone's, everyone's, A lot of people are saying cool things.

I don't know. I have a hard time with this because I feel like I try to get news directly from the company's sources rather than through other people. The reason I say that is because I feel like every time a new model drops, you have a hundred content creators. I'm a content creator myself, so I've probably been guilty of this, but like a hundred people who are just like.

This is the best model ever. Deep seek just changed the game, blah, blah, blah, blah, blah. And then you go in and you play the model, you go read the press releases. Like it's not as exciting as people make it out to be. But I think a lot of people are incentivized for clickbait, which again, I'm a common creator myself.

I get it. So I think in terms of news, I probably go straight to the sources, but I would say I'm following a lot of the big. AI companies just to see what this little, battle plays out to be. 

Richie Cotton: Is anyone else talking about careers in data science at the moment? 

Dawn Choo: Lots of folks. Okay.

People I love for careers in data science. I love SI would have to give, oh gosh, his name's long s. Ani, I will give you his LinkedIn profile. He talks about data analytics, data science. I find him really interesting, very helpful advice and a lot of times he writes nice heartfelt things that kind of reminds you like at the end of the day, we're just humans trying to do our best.

I also like Mary who does more like AI engineering type career stuff. So if you're like trying to begin to AI engineering, she's a good person to follow. This 

Richie Cotton: is Mary Nova. 

Dawn Choo: Mari Nova. Yeah. 

Richie Cotton: Previous DataFramed guest as well. 

Dawn Choo: Oh, nice. Yeah, she's great. Who else? Oh my gosh. I'm sure if I go onto my LinkedIn I'm gonna see like a bunch of people.

I'm just blanking. Karun I think is a good one too. Kohan, I, he does something which. I stay away from doing, which is he takes the most complex AI topics on ML topics and he's I'm just gonna explain this. I don't care if no one likes it. He's willing to take on the difficult topics. I think he's a good person to follow.

Richie Cotton: Okay. Yeah, certainly learning about difficult topics, very useful. And people who can explain those things in simple terms, it's it's wonderful for the rest of us. Excellent. Alright thank you so much for your time, Dawn. That was brilliant. 

Dawn Choo: Thank you. Thanks, Richie. It was so fun being here. I love this.