[DataFramed Careers Series #4]: Acing the Data Science Interview

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You're listening to Data Framed, a podcast by Data Camp. In this show, you'll hear all the latest trends and insights in data science. Whether you're just getting started in your data career or you're a data leader looking to scale data driven decisions in your organization, join us for in depth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in.

Hello everyone, this is Adele, Data Science Educator and Evangelist at datacamp. Welcome to the last day of our four day Data Frame Career series where we deep dive into the ins and outs of launching and building a career in data. I hope you've been enjoying the series so far and I'd love for you to let us know what you've thought about it. So make sure to tag us and let us know what you think. Throughout this series so far, we've really focused in on how to make it to the Data Science interview, whether it's through building a personal brand, which we discussed on yesterday's episode with Quian acing the Data Science Portfolio, which we discussed on Tuesday's episode with Nick Singh, or Optimizing youg Resume, which we discussed on the first episode of the series with Sadie St Lawrence. However, there is no building a career in data without actually solving or acing the interview. This is why I'm so excited about today's guest. Jay Fang is the CEO of Interview Query, a remote data science interview preparation platform who's on a mission to help every data scientist land a job. You can think of it as the leetcode of data science, and Jay has helped tons of data scientists prepare for technical interviews. Throughout our conversation, we discuss the anatomy of a data science interview, the biggest mistakes candidates make during interviews, how to avoid them, and the importance of showcasing communication skills and business acumen during an interview, and much more. If you enjoy this episode or this series, make sure to rate the podcast and leave a review. I'd love to hear from you also just as Sadie and Nick will be delivering talks on June 23's datacamp radar summit, Jay will also deliver a workshop on acing the technical interview. So make sure to register for the event by going to events.datacamp.com radar the link is in the description and registration is free. Now onto today's episode. So I am super excited to talk to you about best practices for acing the data science interview and all that fun stuff. But before, can you give us a bit of a background about yourself and how you got into the data space?

Yeah, so I started out as a undergrad at UW studying electrical engineering, eventually realized I didn't like it that much, got tinkering around with like the circuit boards and the oscilloscopes and realized, you know, I could probably do something more interesting with my time and eventually pivoted to data science by just doing a lot of self learning online. I got into data science through kind of like the rise in 2014, 2015 with pandas and being able to like jump in on Python and analyze all this data and eventually got a marketing analytics job in Silicon Valley when I graduated. And yeah, that kind of spearheaded the whole career into actually becoming a data scientist and then eventually starting my own business in the data science space as well.

That's awesome. And now you run. Can you expand on your business as well?

Yeah, so I'm running Interview Query. So Interview Query is a data science interview prep platform and so we're kind of known as like the lead code for data science. Basically anything you need to know about the data science technical interview, whether it's around case studies, specifically programming, sequel, take home assignments, we kind of put in a lot of practice problems on that and then get you situated into understanding exactly how to tackle and learn these problems from like a first principles kind of thinking.

That's really great. So you're the perfect guest to cover today the data science interview and I want to cover all aspects of the data science interview with you today. So of course data science has matured over the past few years or so. Companies now have a much better idea about what makes a successful data scientist today than they did a few years back and they try to filter out for these skills in the interview process. So given that I want to start off with the first question, I'd love to first unpack what the anatomy of a data science interview looks like today and kind of what are my main skills hopeful applicants need need to showcase throughout the interview process.

Yeah. So data science interviews today are Pretty wide across a range of topics. And I think that's what makes them actually pretty difficult. Like for software engineering interviews you can generally grind leak code and like, you know, study how to basically tackle these programming type algorithm based questions that are tricky but you know, after time they all look the same. Right. And for data science interviews it's very much different. You'll get some probability questions about coin flipping, you'll get statistics problems about distributions. You might get even like a business case study asking you about golf balls in an airplane or something like that. Right. So the difference across it is what makes it difficult. And so a lot of it is about, you know, cementing and understanding exactly which topics are going to be actually important for the interview and are going to come up and specifically what kind of roles you're like specifically looking for as well so that you can cement the specific skills into those roles. And so for example, analytics roles are going to test you a lot more on SQL and data analytical thinking as well as kind of like some product case studies. Whereas machine learning and like more modeling data scientist roles are going to test you a little bit more on machine learning concepts and programming and how you deploy systems as well. And so I think being able to understand those specific skill sets really well and understand kind of like, you know, what your strengths are is really helpful for getting through the interview process.

That's really great and I'm excited to unpack these different topics. But before I kind of want to also ground the discussion on the interview process itself. So given your experience helping folks through the interview process, what do you think is the big misconception people have when it comes to the data science interview?

I think a big misconception is the focus on only technical skills. And I think there's this gray area between, you know, just straight up behavioral questions where they're asking you about culture fit and if you like working with other teammates versus, you know, strong technical skills, there's all these like wide range of questions in between that people don't really expect when they get on the interview. And then they have to perform well on, for example, something that's a little bit more in between is just talking about maybe like past data science projects or defending your resume when you write about your like past data science projects as well. Right. And I think a lot of people kind of expect that. You know, if I do this one project on Kaggle and I follow this tutorial and it involved deep learning, then I can put it on my resume and no one's going to Ask me about it. But you know, that's not entirely true. Right. And I think a lot of the times, you know, if you put a lot of buzzwords on your resume, they're going to expect you to really understand the fundamentals behind that. And that comes into like this kind of gray area between is a behavioral interview question is kind of just talking about your previous experience. What about kind of like case questions where they're asking you about, like, how would you think about, you know, this business kind of strategy as it relates to data science? A lot of those questions are really hard. People don't really expect them, but they're really important for data science roles because you do enact a lot of your judgment in the job and in the interview. And people expect you to have like, you know, 20% better skills and statistics, but also really, really smart kind of judgment. And so I think those things are things that most people don't really think about when it comes to the interview process. They think they can just kind of like grind problems and then kind of repeat them when it comes to the interview. But you'd be surprised at how much, you know, communication, cross functional work is involved in a data science role and how companies also test that.

So in summary, do you think kind of the skill set that you're describing, this gray zone, is kind of a combination of business acumen and data storytelling skills? Is that correct?

Yeah, and I think it's specific towards the role of like an analytics kind of data scientist. I would say that, you know, if you're going for more of a machine learning role or one where you're like very technical, you're just working in code, you don't really have any insight into maybe like the business operation except for the specific requirements that's going into your model, then you might not actually have to deal with this because, you know, they're expecting you to like, do a much better job at deploying machine learning models at scale. But if you're thinking more so of like kind of like this product manager that has like data technical skills as like a data scientist, you know, it's like you don't have to go to someone else to ask them to analyze this data, you can analyze it yourself and come up with a conclusion, then I think that's where you're really expected to know these business skills and these kind of analytical skills to get you through the interview.

That's really great. So let's start off with the technical interview. I'm excited to unpack with you kind of all the types of technical interviews and topics that you can have in a data science interview, arguably this is one of the most discussed and arguably the interview practitioners are worried about the most. So can you walk us through the different types of technical interviews out there for a data science role and the type of topics that you may encounter in a technical interview?

Yeah, so if we talk about like the different kinds of technical topics, I can go through probably like 10 different ones if we want to go through them all. But specifically, I think where it starts out is there's more like the case study ones, right? That's more like product metrics, data analytics and business case. And so these are more so questions that will ask you about investigating different anomalies, understanding success. How would you like kind of a B test, a feature? Those kinds of questions are very much focused around this kind of, you know, product manager, data scientists, data analytics hybrid. And those are I think, one of the ones that we focus on a lot at Interview Query because they're kind of like the sneaky ones that you don't really know if you got the right answer or not. Arguably there really isn't a right answer. If I ask you, how would you measure the success of the Data Frame podcast? We could probably talk for a really long time about all these different ways that Data Frame podcast contributes towards data camps overarching goals and mission. Right. And so these are the ones that I think are the most tricky for most data scientists. Then we kind of get into more of the technical case study stuff. And so that's more. So we're talking about machine learning concepts, potentially even deep learning. This is even like probability statistics. These types of questions all kind of go around this mathematical and conceptual idea around, like, how much do you know machine learning? Right. And so this could be like you have a model that you want to build for, let's say, predicting the amount of subscribers you're going to get on a podcast. How would you build this model? Which kind of model would you use? How would you evaluate your model? And so it's kind of like a full walkthrough of how much you know about machine learning when it contributes to an actual business versus a model. And it contributes to a very specific kind of technical, you know, just case study around like it's not going to be the titanic data set. It's going to be something that the company has to deal with as well. The last round of kind of like interview questions is then programming questions, SQL questions, data cleaning questions. And so these are all around kind of like how quickly are your actual core technical skills, right? If I ask you to like draft this report or get this metric, like how quickly can you do that? Are you going to take a lot of time and are you going to mess it up? Are you going to give me data quality issues or are you really sound technically and can you apply these theories, set theory in practice and really retrieve the data that I'm looking for? That's probably the last grouping of technical interview skills.

That's really great given the breadth of technical topics available. You mentioned here at least three big categories of different sub skills that you can have and the different skills data roles. From different companies to companies, how should applicants prioritize which technical topics they should prepare for in their interview process?

Yeah, so I think one big thing that an applicant should first do is just go through the role description and understand exactly what kind of role it is. Right. Like we kind of talked about like the two to three different kinds of data science roles and so just understanding, you know, am I under like this product and analytics team or am I under this more machine learning heavy kind of team or is this like a startup where I'm doing everything right? And then I think it becomes more understanding the interview experiences previously given at that company. So if you can read through and understand, you know, what previous interview experiences other candidates have gone through, you can kind of get an understanding of like just what actual data science roles and skills are going to be asked. We do this thing at Interview Query too where we've created these guides and we've really worked on making these guides good in terms of understanding. We scrape a lot of interview experiences, we kind of do some classification and we just kind of show a radar chart of exactly, you know, what skills are going to come up for each interview. Just so applicants a good idea of which question types they need to actually practice as well. And then you know, the last thing to do is just to make sure you can benchmark your skill set across these different question types. So if you know, you know that they're going to ask a lot of SQL questions, then definitely practice like a ton of SQL questions. See how good you are at SQL. If you can figure out you're in like you can pass, you know, like some easy, medium hard questions on leetcode or Interview Query and you know, the questions you ask at the interview are going to be around like easy to medium, then you should be good to go, right? So just really understand, you know, your skill set and which, which ones you need to study for the most.

And if a company, let's say, doesn't have a lot of kind of best practices or a lot of available online resources on what the interview process would look like, what would be your advice for someone kind of looking to understand and prioritize which direction they should go for in preparation for a particular interview?

Best way to overcome that is just honestly to talk to the recruiter or hiring manager and ask them what they test for and just say something like, I want to know that this is a good fit for my skill set. Could you please go over what kind of role this is or what might be on the interview so that I can be well prepared? And that should be pretty good enough. It's very understanding that you might want to know what's going to be asked. I mean, I think most people know that these interviews are pretty random across potentially 10 different topics. And just asking that question shouldn't hurt.

That's great. So you've helped a lot of people kind of go through the interview process. I wonder, from your perspective, what do you think are the most common mistakes applicants make during the technical interview? You.

So the most common mistake that I've seen is that candidates generally will get tripped up and jump right into a question when they should probably take a second to think about it first. I think there's this idea around measuring your success in an interview across three different specific areas, right? One is kind of business acumen, another is communication, and the last one is like technical intuition, right? And so across those three, all the times people kind of jump right into like the technical part. They like, you ask them to build a model and they jump right in into like the technical details around, you know, random forest or something like that, when they should actually just be gaining more information to understand the actual situation that they're in. A lot of the times you're in kind of like this information disadvantage when you're working in an interview, right? You're in this like 45 minute close time slot where basically the interviewer is asking you about a problem that you've never heard about or a situation that you've never even thought about in your entire life. And then you suddenly have to kind of like come up with an answer that's just as good as if they were maybe talking to a coworker. With that kind of disadvantage, you know, you really have to get a lot of questions. You have to get a lot of like, answers and a lot of context really quickly. And so I would always advise to, you know, ask questions, take Some time to really deeply think about the problem and then structure your answer in a way that's kind of cohesive and allows it for you to state a lot of assumptions upfront so that you don't make some, you know, really kind of dumb mistake by just diving into an area that is completely different than what they expected.

I completely agree. Especially jumping into the technical part, you miss out on a lot of context that could help shape even the type of technical solution that you want to provide within the interview process. So can you walk us through kind of an example of this common mistake that you're talking about from real interviews that you've seen and what would have been a better alternative?

Yeah. So, for example, there's one common question at Facebook that's something like, you know, Facebook is seeing that parents are joining the platform to and adding, like, their teenagers as family members in the app. Right. And how exactly do you measure, like, the effect this has on teenagers? And a lot of people will jump directly into just saying about how they would, like, build a model to basically see the teenagers churning off of the platform. And that's not really like the right way to frame it. Right. Because a lot of the times I would say initially, like, how do you even know that the parents and the teenagers are linked up? Like, how do you even have that data to understand that this parent is like a parent of this teenager? How do you know that their family members. How do you know the age of these different users? And so just like clarifying a few things, understanding your data set and then deriving some causal relationships afterwards is probably a better bet. But yeah, I think it really does depend on how you think about the problem. And I think sometimes if you can successfully argue your point, then that should be fine. If you're extremely technical person and they know that they're hiring you for f. For extremely technical role, then they should be okay if you kind of jump into like this deep learning model or something like that. But otherwise, for everyone else, that's kind of like tackling these questions. You really want to kind of apply that judgment and be a little bit more forward with how you approach things.

So given that, what would be your recommendations kind of for a study plan to be able to shore up some of these gaps technically minded people may have that they don't necessarily have in the same sense around kind of this product sense or being able to approach problems constructively?

Yeah. So I would say that the best thing you can do is to benchmark kind of your skill set. As I said before and just understand like what level you have to be at to do. Well the best way to do this that we've seen is to actually conduct kind of like a coaching session with someone who's like seen a lot of people that has passed interview or hasn't passed the interview and they would then kind of understand the level of what you need to be at. And so if you can kind of do an initial mock interview on like a couple different question topics and get like that benchmark, especially for those questions that are a little bit more open ended, then you can like understand basically how much you know, content you probably need to then study to then get to that level. You know, if they give you you know, like 3 out of 10 rating basically and this is what we do, interview query as well, we basically assign kind of like a score after the end of the mock interview. Then you know that like, basically you have to probably do like X amount of more studying. And so generally for us, you know, like if you're at the 50% mark, you probably need to study you know, like for another three to four weeks. Right. But if you're already at like the 80th 90th percentile in terms of like how well you're doing on like a particular question that they asked, then you probably just don't need to study that much. You probably only do like one to two more questions. And so it's really about understanding just like your level when you're given different like problem types.

Okay, that's perfect. So let's jump over from the technical interview to discuss more kind of the behavioral interview, slash data storytelling business interview to a certain extent, arguably one of the most important aspects of the interview process is showing your ability to communicate. We touched on this earlier, but I'd love to expand on this by getting from you just how important is this skill from a hiring manager's perspective and why?

I think it's super important. I think a lot of the times I've actually heard that this is like the biggest red flag that they get from candidates. A lot of them will be able to pass like the SQL interview, but then when it comes to just behavioral questions, they'll stumble a little bit. And the reasoning before that is kind of like what we talked about, kind of displaying judgment and being able to communicate your findings. This kind of like thing about like if you can't communicate your findings well, did you even analyze the problem? Right. It's kind of like if a tree falls, did anyone hear it? Sort of question right it doesn't really matter if you do a really good job of an analysis if no one can really understand it. And so a lot of the times I think hiring managers really want to see that, like, ability to communicate insights and be able to craft that kind of data storytelling ability so that other people understand what you're trying to convey. And especially at bigger organizations, you have a lot of different people at these companies. Right. You might have to convince like hundreds, maybe in thousands of people to like, actually like, read your analysis, maybe take something away from it and understand something to do that requires, you know, pretty good skills in terms of either writing or communication. And kind of keeping that in mind, I think that's probably why it's such an important skill for, you know, all these kind of technical roles. And I guess even if you're on like a very technical role, like a machine learning, engineering, deeply research scientist role, you still also have to understand exactly how to communicate technical concepts within your team and work together as well.

And I completely agree with that notion because gaining adoption as a data scientist is what makes or breaks kind of your work down the line once you work in a company. So as you said, your analysis doesn't really matter if no one's being able to consume it or if you're not able to create decisions or generate decisions as a consequence of it.

Exactly. And I think that this is pretty underrated. I think also increasingly, as we become kind of integrated into a remote first world, this is going to be even more important. Right. In terms of just like written communication. And this is going to be a lot more widespread, I think, in terms of how it affects just general, like, workplace culture. So getting a leg up on that, having good, you know, writing and communication skills is always like, pretty necessary, I think, for any technical role.

That's great. So similar to my previous question on the technical interview, what do you think are the most common mistakes applicants make when showcasing their business acumen and communication skills in an interview process?

The biggest mistakes, I think, and I think I've kind of touched on this too, is just kind of jumping right into it, I would say additional things that generally could be worked on is also just kind of like conversationally structuring your answer so that it flows well. A lot of people have this kind of jumbled thought. I'm definitely one of those two in which, you know, if I think of an idea, I'll kind of jump in and say it, but I won't really think about how it relates towards like the overall structure. And you Know, in writing, we have this whole, like, concept of, like, an intro, a body, and a conclusion. But that's really hard to do when you're just, you know, speaking to someone conversationally and you're, like, creating this pitch out of nowhere. So just adding a bit more, like, you know, structure towards, like, your thinking definitely goes a long ways towards the other person understanding it a little bit better.

I completely agree. Especially, like, on that kind of structuring your conversational thought. It's definitely a difficult skill to master. And given that communication skills are inherently not as straightforward to improve as technical skills, how do you recommend people to kind of shore up these gaps and especially as they go through the interview.

Process, definitely the number one thing you can do is just to do more mock interviews, whether that's with peers, coworkers, or even, like, professional coaches online. Just getting in that repetition format of basically repeating, like, the same situation of the interview but, you know, without as many stakes is, like, a great way to, like, improve. And I think just like, doing that again and again is, like, probably the best way to tackle it, because this is kind of, like a weird foreign concept. Usually people don't ask you for, like, your technical input on, like, a question that you've never heard of. Right. And ask you to respond within, like, 20 minutes on, like, how you would approach it. And so you're doing a lot of, like, deep thinking, and you're also having to, like, respond to it and then structure your thoughts and then also, like, make sure that it makes sense. And then you have to jump into, like, another kind of realm if the interviewer asks you a different question. And so overall, I would say just doing more practice of that is the best way that you can, you know, get better. I mean. I mean, you could also just say, like, talking and socializing with people more, but honestly, it doesn't really help with that specific skill of, like, deeply thinking about a subject and then also having to then craft, like, a solution to it.

That's great. And in terms of kind of you want to break down that skill, what do you think is more important today, like, writing and speaking or being able to deliver presentations? Like, how would you break down kind of communication skills into its subcomponents?

I would say writing is probably the most important in this day and age, just because I think everything kind of starts from writing just to begin with. Like, you kind of have to start writing down your thoughts and structuring them when you're doing an interview. It's definitely helpful, even if you do A presentation. You're also writing down your thoughts and structuring them beforehand. At least I'd hope so before, you know, kind of jumping in and talking about this, like, long graph or like a bunch of different insights that you've had. And so I would say that writing is probably the most important. It's also the most beneficial to be honest in this day and age when everything's asynchronous. And, you know, for me, like, I found it to be really helpful. I can just post on LinkedIn, I can write blog posts. People will read it throughout the years and then come to visit our website and kind of like, want to then kind of read more of our content. And so for me, it's been like the core crux to like, building my whole business on interview query and has also helped me throughout my career in terms of just making sure that like, anyone who with some like, sort of power or sway at responsibility at like an organization such as, like the CEO or the executive team, will be able to see that I have, like, insights to contribute. Right. And especially I think with writing, it's just a lot easier for you to kind of just communicate those technical thoughts and save them somewhere for you to, like, look back on as well.

I couldn't agree more. Especially kind of when you think about the importance of a portfolio project and how important are kind of portfolio projects getting your foot through the door through the interview process. For example, if you're able to showcase great storytelling skills throughout your portfolios, throughout your written content, you have a much better chance of making it past the finish line than other candidates. Would you say that's correct?

I think so. I think just doing the portfolio project gives you that practice towards becoming a better data scientist as well. I would say that portfolio projects are a little bit hard to get in front of people's eyes unless they have some sort of, like, additional kind of new insights or if you can market it really well because of the fact it is really hard to like, for someone to just look at your resume and actually, like, analyze it versus, you know, just looking at your resume for two seconds and then kind of moving on to the next one. And so I think, like, portfolio projects are always good and the main reason for that, and personally is just through the practice and the repetition of actually doing more data science. But additionally, you know, as you said, for you to actually communicate those insights into like, writing and have it saved somewhere, that's also like the second step that you really need to do for these projects to like, make them last and also, like, build that repetition feedback loop in your head.

That's awesome. So we covered the technical interview, we covered the behavioral interview. Finally, I'd love to dedicate kind of this last section of our podcast. Just chatting about the general best practices throughout the interview process itself. Let's say I made it through the final round. I'm having a final fitcon conversation. What are questions I should be asking as an aspiring data scientist to know whether this role is the right fit for me?

I would say that there are these general values that a company has, and then there's also, like, specific values that like a data science team might have. And so just understanding what those values are for yourself and seeing if those exist at the company and within the team is a great way to kind of, like, get started. There's this great website called keyvalues.com and the founders, like, kind of made this into, like a job board unintentionally. But essentially you can go there and you can see kind of like company values of what companies kind of like, choose in terms of, like, great work culture, work life balance, or like, good code. And you can kind of like choose your own from there, and then you can find like, the companies that actually have those values. So I love that website. I always recommend it. And I think that those are like, great kind of like lists of items that you can go and ask companies about if they practice basically in the interview as well.

That's really great. And finally, I think one sticking point throughout the final fit and like, once you receive the offer and you have an offer conversation is salary negotiations. I think this is something especially junior candidates struggle with. Do you mind walking us through how you'd approach salary negotiations, how you've consulted other interviewees that you've worked with to approach salary negotiations?

Yeah, so definitely, I think the biggest thing is understanding your own market value out there. So generally, companies will hire you for the price that it costs to replace you. That's kind of like the brutal nature of hiring in general. And so for a candidate, how you can take that to your advantage is to understand, you know, what your market value actually is. And so we actually just released this kind of salary report on interview query, where you can go in, you can type in kind of your location, your role, maybe even a company, and kind of see what the average salary is across a bunch of different ranges. And I think that will really help towards understanding exactly what you're probably going to be expecting for this specific role in salary negotiation in terms of actual Salary negotiation tactics. I would say that one thing that you could do is also just delay the conversation around salary until as late as possible, just because it doesn't really help you when you talk about it earlier in the process. Mainly I think it actually kind of hurts you because you're giving them information up front. One thing I've learned is that you can always ask about salary ranges, especially with new laws kind of coming up for a position. And then lastly, one thing that you can also do is kind of negotiate on different things that are not really akin to just salary. And so there's base salary, there's stocks, there's RSUs, there's like PTO, there's 401k matching, there's all these things that you don't think you can negotiate on, but you actually can. And I think, you know, I think it's something that's available and will help with like the negotiation process.

That's really great. And harping on that salary insights report that you guys released, what were some of the key insights that surprised you at least once? The insights were distilled.

Yeah, the key insights that surprised me the most. And personally, as just like a stat geek, I really like the fact that that one, there's all these cities that people can move to that basically have a better ROI on their salary than like the top cities you think of. So if you think of like San Francisco Bay area, Seattle, right, they're going to pay you probably the most. But if you move to cities like Boise, Salt Lake City, a lot of cities in Texas, you actually get better, like ROI on your money. From a cost of living perspective, these cities are all like kind of great upcoming cities, probably better. Like also real estate investment opportunities if you're going to move there. But also just in general with the remote work, I think they've like grown a lot in popularity. The second big insight was the fact that data science salaries on average have gone up around like 25% in the past five years, which is pretty incredible because compared to software engineering, salaries have only gone up by like around 6 to like 8%. And so it's a little bit harder if you try to measure total compensation. But specifically, I think the biggest data science roles that I've seen the most growth have been data analytics and data engineering. And both of these roles have skyrocketed up and gone up by about 35% in the past five years. Data engineering, I think before is misleading because a lot of the times data engineers are actually software engineers and software engineers are Data engineers and they just don't distinguish them on roles or on LinkedIn so that you could really analyze the data Intel. But this role of data engineering is blowing up, right? And people are paying more for it and they really, really care about getting good data engineers. Because as we all know, you need good data engineers to really enable good data scientists as well.

That's really great. And I love that first point, especially on like the different cities you've lived in. We've definitely been excited as well around the remote work revolution and what that means to kind of leveling the playing field one when it comes to talent search and kind of breaking into data science, but also getting like the best type of compensation you can get for where you are now. On a final note, Jay, let's say you are a data scientist who just joined a company. How do you provide value and make a good impression in your first 90 days?

I always tell everyone, like in your first 90 days, from what I've learned, basically just go around and meet every single person that you think is important and then ask them who you should meet basically and then just keep on meeting people. So you just basically go around kind of intro to everyone that you know. Then definitely just try to add some business value like extremely quickly. And this is like not very difficult because there's a lot of small things that people just overlook and just don't do. And so one thing could just be like documentation, like just going in and documenting something that you're finding out isn't documented on your onboarding. That should probably be documented. And people like, that's huge value, right? Because honestly this is just something that people just don't do and just need to be done in the onboarding process. Then the second thing I think is just kind of just figuring out incremental ways to like continue to add value on top of that maybe even from a tech perspective. So personally for me, like kind of like doing some writing, doing some analysis, kind of sending that out to team members afterwards from like your first experience is good. And then also just contributing like small pain points where people feel like, you know, they either need like a script for something or they, let's say that they like need, yeah, an ETL job somewhere. Just like kind of figuring out exactly like how you can most provide like these small kind of like value add activities is like a great way to kind of start out. And then I think as that kind of ball starts to roll, then a lot more things will be a lot more apparent and then you'll Kind of take on bigger projects. But I think it's really important to just have a few like, success kind of like wins under your belt so that you can be known as someone who like, you know, does stuff basically. I think sometimes, you know, you might struggle as a junior candidate where you try to like take on this huge project right when you get started. And I wouldn't recommend on that. It's more important to kind of learn a little bit of the ropes and kind of like get some wins under your belt first.

I couldn't agree. That kind of low hanging fruit approach to impressing on your first 60 days is super important, especially in smaller organizations where kind of the time to value is a bit shorter than like larger organizations to a certain extent where you're expected to have like a longer onboarding time. And that I think as a framework has helped me maneuver my career quite a lot as well. How do you kind of systematize choosing which problems to tackle within your first 30 to 60 days?

The information disadvantage, as you said, is huge. It's like basically the time to value is really, really high. And so the kind of approach that you could take maybe is kind of like list down all the problems that you see, kind of assess the scope of each one and then as you said, kind of tackled the low hanging fruit first. Right. And ideally, you know, like, maybe it won't be like a huge value add to someone or highest priority, but if it's like an easy one day, few hours kind of win, then like definitely notch that under your belt and like just prove that you can, you know, add value to this organization. And I think just like slowly moving up in terms of actual projects after you're looking at, you know, that whole list is probably the best thing you can do. Right. They're not expecting you to lead a project like 30 days out. Right. And you probably shouldn't be doing something like that to begin with because you probably don't know a lot about the organization and like a lot about the business. And so it's all about like kind of slowly kind of tackling projects and like, I think gaining that success under your belt for sure.

That's really great. Finally, Jay, I had an awesome time chatting. Do you have any final words before we wrap up today's episode?

Not really. I would say for everyone who is interviewing out there, I definitely know that the mental toll is very high. This is something we deal with a lot, interview queries that we like, hear about people. You know, you get an interview, you get excited, you like, go all the way to the on site and then you fail. And then it's crushing, right, because you're just continuously kind of getting hammered in these, like, interviews from, like, a mental perspective and then also from like, the fact that of you're getting a lot of rejections. And so I say to always, like, stick in there. You know, it's. It might seem like it's like, really bad, but actually all these rejections on the interview is actually just, you know, kind of like, you know, more and more practice, so you could say towards getting more and more offers at that point later down the line. And it's definitely a grind. I would hope that, you know, you can find some ways to make it a little bit better. I personally do a lot of things where I try to concentrate my focus around things I do enjoy and figure out how I can incorporate that into like, something that is a grind, like interviewing. And I think that, like, works well personally for me. So, yeah, I would say just trying to integrate it. Don't be too hard on yourself and then definitely realize that you'll eventually, throughout the grind, there is like an opening at the other end where you will eventually find a job.

That's really awesome. Thank you so much, Jay, for coming on the podcast.

Yeah, thanks, Adele. Let's talk soon.

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