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Q&A with a Data Science Hiring Manager

  • 27 February 2019
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Q&A with a Data Science Hiring Manager
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Everything you've always wanted to know about the data science hiring process but were too afraid to ask!

Dan Saber, Data Science Manager at Coursera, wants to answer your questions about the data science hiring process. He's happy to share tips for getting hired as a data scientist and more general data science career tips – just ask!

Between now and 8 March, reply to this post with your questions for Dan and he will respond by 18 March.

About the Q&A Host
Dan Saber is a Data Science Manager at Coursera, where he leads a team that develops the insights and algorithms powering Coursera's top-of-funnel growth. Before joining Coursera in 2014, Dan was a fellow at Zipfian Academy, a 12-week intensive Data Science program where he turned his theoretical statistical and econometric knowledge into real-world Data Science skills. In a past life, Dan worked in Investment Management, where he analyzed the debt securities of high yield companies while wishing he'd chosen to do something less soul-crushing. He attended UCLA, where he earned a degree in Mathematics and Economics.

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46 replies

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  • What are the most important skills you look for in a Data Scientist?
  • What could a candidate do to stand out from the crowd?
  • What are your tips for keeping up with the pace of change in technology and specifically Data Science?
Userlevel 1
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  • For a BSc student who is looking for a summer internship, what skills are targeted?
  • What are some good places that offer internships in Data Science for international students?
  • Any general advice from an expert's point of view for students ?
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  • Which Coursera courses/specialization will make someone most likely to get hired? Could you please rank them?
  • What are the minimum requirements for someone to be considered for a data scientist role?
  • How likely is it that, someone without a data analyst/business analyst experience, may be hired for a data scientist position?
Userlevel 1
Usually a career break is a red flag for hiring managers sometimes is relevant to older workers, say 50+...
in your experience, or your perspective, what are the factors that would make you consider an applicant who had a career break of 5 years? What are the things that would make you DQ this candidate outright?
Likewise, what are the expectations for a career transitioner, whose prior experience is in engineering, but not in data science, nor directly in code forging?
Finally, what is the list of - maybe 5 - challenges that a hiring manager would need to overcome to put such a candidate into consideration against the continually employed, younger, coding adept, current, non-transitioner? Is there any value in a tangential perspective, coming from an unrelated but still technical/mathematically adept discipline (electrical engineering, or quality engineering, or accounting for example)
No sugar coating is necessary. Thank you.
Userlevel 3
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- What are some things that people who are looking to get into genomic data science consider?

- What obstacles do non-degreed data scientists face?

- How feasible is remote or freelance work?

- What kind of experience can someone get in order to beef up their salary?
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What do you expect from a recent grad when you ask questions like - where do you see yourself in 2 years from now ? I have been asked this question recently during an onsite interview for a start up.
Userlevel 1
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For recent graduates with so industrial experience in data science - what kind of skills you suggest to master considering the demand of industry ?
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I keep hearing that data scientists is the "sexiest job" now and commands a high salary, but where I live (Asia), the demand for data scientists is not only few, but the high salaries and abundance of job opportunities are also limited to startups (local or global) and unsafe places to reside in.

Data scientists positions, where I live, command at least 3 - 8 years of experience - with the mean around 5 years. I'm working as a data scientist cum web developer for an educational initiative at a university, with a decent salary and a satisfactory work-life balance, but there could be more than this.

I have a Master's Degree in Computer Science and I graduated as a Dean's List recipient.

Questions:
  • I'm not too keen to work for startups because an alumnus said they are a trap. Do you have good reasons to convince me otherwise? Such as, how might a good startup support my career growth? Or, how do I tell apart a good startup from a destructive one?
  • Do I seriously need a PhD to get ahead in the field of data science? (A professor tried to hammer into me the necessity of a PhD in a quantitative field because so many people of my ethnicity have one and I'd lose out if I didn't. But I also know from other sources that a PhD is far from a ticket to success.) Or is my Master's degree enough to get me hired as a high-paying data scientist provided I have a good track record / portfolio? What constitutes a good track record / portfolio?
  • Since certain policies forbid those of my ethnicity from entering the US, where the aforementioned professor said the best data science PhDs degrees are present, if the answer to the question immediately above this one is yes, where are suitable alternatives to studying in the US? Or do I not need a PhD at all?
  • How do I find time to build my portfolio of data science projects? Where can I find the passion for this sort of thing, while maintaining a good work-life balance and not have to disengage with things I love?
  • How often should I contribute to open source repos (such as those on GitHub) to prove to prospective employers that I meet the requirement of having "open source community contributions / contributions to collaborative projects"? How should I reflect that on my CV and/or cover letter? (Are CVs and cover letters necessary now?)
  • How should I prepare for overseas employment as a data scientist? What should I pay attention to, and what should I be less stressed about? What are common pitfalls that data scientists being hired outside their region of residence fall into, and how do I avoid them?
Thanks again.
Userlevel 2
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  • What are the most important skills you look for in a Data Scientist?
  • What could a candidate do to stand out from the crowd?
  • What are your tips for keeping up with the pace of change in technology and specifically Data Science?


Hi Liz,

Thank you so much for your questions!

I'll combine the first two about skills and standing out. In general, we like to look for 'T-shaped' Data Scientists, which is to say: Data Scientists who have a solid foundation in key data skills -- statistics, programming, machine learning, and analytical intuition -- and combine that with a "unique" differentiator that complements the team.

That "unique differentiator" could be anything. Perhaps it's domain expertise. Perhaps it's knowledge of causal inference. Perhaps it's communication ability, or data engineering ability, or even a knack for building interesting Shiny apps. The cool thing about Data Science is that good Data teams bring people from a variety of backgrounds together, so thinking about how you fit into the larger whole is a strong way of standing out.

(Indeed, I think this is good career advice in general: Think not just about your "primary" skill set, but also how your primary skill set combines with your secondary and tertiary skill sets to produce a combination that is uniquely you.)

That said, if you're just starting out, I would of course recommend mastering a handful of basics (i.e., the base of the 'T') first: learn one of R or Python *really* well; learn database manipulation (i.e., SQL); learn basic statistics (e.g., hypothesis testing, regression modeling, common distributions). More importantly, learn how to apply all of the above to *real* analysis and modeling problems. If a junior candidate could do that, I'd be impressed. (Plus, Data Science is such a broad field that if you try to master all of it at the same time, you'd be learning forever!)

To your last question, I think am legally obliged to answer Coursera :)

Seriously, though, I'll give my experience, because it's changed as I've progressed through my career. Early on, I took a ton of online courses to build out the base of the 'T.' (In particular, I came from a Math/Economics background, so I knew nothing about Machine Learning. Thus, I spent a lot of time learning ML techniques.) Now that I'm working, I rely more on "just-in-time" learning, e.g., "I need to solve some specific problem at work. How do I do it?" Those questions then become my learning guide. Needing to learn something in order to your job is especially motivating 🙂
Userlevel 3
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Q1 : What should an undergraduate student like me do to start a career in data science?
Q2 : Will auto-ml affect job market in data science ?
Q3 : What are the data science skills/tools that are considered for hiring?
Q4 : What are the other computer science skills/tools required? (like cloud , software development , algorithms etc )
Q5 : Most of the computer science hiring processes have competitive programming as their preliminary round. Is competitive programming (like code forces) important in data science hiring process too?
Q6 : How valuable is a online course certificate considered during hiring?
Userlevel 6
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Hi Dan,

1.What are the challenges that we face when you initially enter into the Data Science Job. How should we overcome those challenges?

Thanks
Kalyan
Hi Dan,

Here's my question: In your opinion, how old is too old to switch over and get hired as a Data Scientist, not only in Coursera but in other companies as well? 35? 40? 45?

I'm asking because I attended a couple of webinars in the past where career switchers with significant IT experience are frustrated that they could not land a job in AI/ML field because, and in some cases they were told, that they are too old. I'm in the same boat, and want to learn more, but with this in mind, I cannot help but have second thoughts.

Thanks a lot, and have a nice day!

regards,
Alan
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So many good questions! :)

I need to head into work, but I look forward to answering more tomorrow and the rest next week!
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Whew, what a journey! Thank you again for all of the wonderful questions all. This was a ton of fun 🙂
There are many new graduate students here, we would appreciate if you can show us a way to start things or skills to obtain.

Thanks
Emre
Badge +1
  • While hiring Data Scientist how to differentiate the role communicated, e.g. ML engineer vs Deep Learning Engineer vs AI Algorithm developer vs Data Scientists doing Analytics/visualization/reporting?
  • Do you expect them to solve puzzles/tower of honoi/search/sort type of problems during interview in addition to all the ML/DL/Stats thing?
How could a data scientist improve himself on business problem-solving?

There are some good material/courses about applying data science to solve business problems? One can know all about the algorithms, the features, the training of a model, but being able to solve the actual company problem and turn these into profit is something else.

Thanks and my best regards.
Hello dan,
My question is how do you judge approach of a candidate, in solving a data science problem, and according to you what are the most common approaches preferred by interviewer.
  • Do you use take home challenges in hiring? If so, what do you look for? What are your general thoughts on take home challenges?
  • What has changed for you in going from a data scientist to a data science manager? What are the skills you needed to demonstrate to make that jump?
  • What is the split in how much of your team's work is on doing analytics looking at the past and predictive analytics?
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What do you expect from a recent grad when you ask questions like - where do you see yourself in 2 years from now ? I have been asked this question recently during an onsite interview for a start up.

Thank you for the question!

My general advice here is to tell a compelling story: People love digestible narratives. More importantly, they love hearing how they fit into your narrative :)

Concretely, what I mean is to explain how the opportunity you're interviewing for fits into your broader career arc, e.g., "I've done X and Y [in school/internships/personal projects/etc.]. I think I would like to become Z, and this opportunity excites me because I think it will help me develop the skills I need to become Z."

Of course, don't fear if you're not sure where you want to be in the future! I think most people are generally sympathetic to the idea that the future is intrinsically uncertain, especially for a new graduate :)

(FWIW, I don't typically ask that question in interviews, but if I did, it would be because I want to ensure that the role you're interviewing for could help you accomplish your career goals. I'd hate for you to have a well-defined career goal that simply wouldn't be well-served by the opportunities I have available.)
Userlevel 2
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  • While hiring Data Scientist how to differentiate the role communicated, e.g. ML engineer vs Deep Learning Engineer vs AI Algorithm developer vs Data Scientists doing Analytics/visualization/reporting?
  • Do you expect them to solve puzzles/tower of honoi/search/sort type of problems during interview in addition to all the ML/DL/Stats thing?


Thank you for the questions! Answers below:

  • While hiring Data Scientist how to differentiate the role communicated, e.g. ML engineer vs Deep Learning Engineer vs AI Algorithm developer vs Data Scientists doing Analytics/visualization/reporting?


At Coursera, we try to ensure that we're very clear about what the responsibilities of any given role are. To this end, we break down our Data Team into three main functions: Data Engineering, Data Products, and Decision Science.

  • Data Engineering builds our core data warehouse and maintains our self-serve BI tooling (e.g., Looker, Tableau).
  • Data Products does "Data Science for Machines," i.e., they build models that power some kind of onsite application (e.g., they build recommendation engines, algorithms that improve search, etc.)
  • Decision Science does "Data Science for Humans," i.e., they perform analyses and build models that help business or product stakeholders make important decisions (e.g., they analyze experiments, suggest productive improvements, analyze the impact of X feature among Y group of users, etc.)
The output of Data Products work is usually some kind of API that product engineers can call to personalize the onsite experience. The output of Decision Science work is usually some kind of Jupyter Notebook or presentation that makes a strategic recommendation.

Of course, this isn't the only way of separating Data Science roles. Airbnb, for example, splits their team into Analytics, Inference, and Algorithms. (At Coursera, we combine "Analytics" and "Inference" into "Decision Science," with the expectation that our cross-functional partners can do most of their own analytics through our self-serve BI tools mentioned above.)

My advice here would be to ask precisely what a given company has in mind when they say "Data Scientist." Ask, "Am I going to be building models that will be put in production?" "Am I going to be analyzing data to help Product Managers or Marketers make decisions?" "Am I going to be need engineering chops, or are you really looking for a statistician?"

Unfortunately, job postings can often be very unhelpful in distinguishing among these roles. That said, there are usually keywords that signal whether a given opportunity is Data Products or Decision Science. (Data Products: Machine Learning, Algorithms, Engineering, etc. Decision Science: Analytics, A/B Testing, Statistics, etc.)

  • Do you expect them to solve puzzles/tower of honoi/search/sort type of problems during interview in addition to all the ML/DL/Stats thing?


We don't, and FWIW, I didn't encounter these types of problems during my job hunt. (That said, I wasn't interviewing for ML Engineering roles, and it's possible they'd be more common in that realm.)

Hope this was helpful -- thanks again!
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I've been analyzing wind turbine noise data for about 2 years. This is a very specialized field therefore the number of job openings in this area is also limited. When I apply to data scientist positions from other domains (like gaming for example) I get rejected indicating the lack of my domain knowledge.

How can I convince a hiring manager that I can learn about the domain (although it's new to me) and be effective in the job?


Great question, Thileepan!

I would lean on some advice I already gave (e.g., develop a DS portfolio, etc.). However, in your case, you're lucky in that your experience is already analytical.

I'm not necessarily sure if this applies to you, but one thing I've noticed in folks transitioning from more "niche"/specialized fields to Data Science is that their resume/cover letters/etc. sound too much like they're optimizing for their "past" career, not the career they want.

To put it bluntly, hiring managers aren't always the most creative people. They're trying to find someone they think can do the job as quickly as possible, and if a resume or cover letter requires them to think creatively (e.g., "Well, this candidate doesn't have experience in my domain, but their field *sounds* technical, so maybe it's worth a shot..."), it's going to reduce your changes of getting called back.

In my case, for example, when I was transitioning from Finance, I learned that my resume was too "finance"-y. In particular, it talked a lot about backtesting algorithms on historical S&P 500 data, and tech companies didn't care about backtesting algorithms on historical S&P 500 data. However, they might have cared about my ability to build and deploy models that brought value to our cross-functional stakeholders.

In short, a job application is an opportunity to tell a story, and when you're transitioning from one field to another, the story should be: "Here's how my experience uniquely prepares me for helping you solve the challenges you're facing." People are usually amenable to compelling narratives, and that's a compelling narrative.

(One additional note: to pull this off, it's important to develop a fluency with the jargon of the company/industry you're pursuing. Networking is a great way of learning that jargon 🙂
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How could a data scientist improve himself on business problem-solving?

There are some good material/courses about applying data science to solve business problems? One can know all about the algorithms, the features, the training of a model, but being able to solve the actual company problem and turn these into profit is something else.

Thanks and my best regards.


Thank you for the thoughtful question, Danilo!

I think your point is right on: Thinking about your work in terms of business impact is absolutely critical. If you have that lens, you'll always be a valuable team member. (The number of Data Scientists who optimize for what's technically interesting is surprisingly high.)

In general, I'm not sure if there are any shortcuts here (or if there, I haven't found any :)

My approach/general principle has been learning as much as I can about the domains of the people I'm partnering with.

For example, at one point, I began working with our Marketing team more and more often. To better understand their world, I took Coursera's Digital Marketing Specialization. Learning what problems Marketers cared about enabled me to apply my Data Science skill set to their problems (not what I thought their problems were), which enabled me to bring a ton of value. (In particular, as I was going through the course, I kept thinking, "Oh, Data Science can be useful here, here, and here," or, "Wow, we could do these kinds of analyses easily.")

Similarly, when I began working with the Growth Team, I learned as much as I could about Consumer Growth by reading the blog posts of Andrew Chen, along with every book on growth hacking I could find on Amazon.

Boiling it down to some general principle, I'd say this: As Data Scientists, we have a ton of unique skills, but they're only valuable when they're intersected with some kind of domain knowledge. The trick, then, is building that domain knowledge. As you do, problems to solve will naturally emerge.
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Usually a career break is a red flag for hiring managers sometimes is relevant to older workers, say 50+...
in your experience, or your perspective, what are the factors that would make you consider an applicant who had a career break of 5 years? What are the things that would make you DQ this candidate outright?
Likewise, what are the expectations for a career transitioner, whose prior experience is in engineering, but not in data science, nor directly in code forging?
Finally, what is the list of - maybe 5 - challenges that a hiring manager would need to overcome to put such a candidate into consideration against the continually employed, younger, coding adept, current, non-transitioner? Is there any value in a tangential perspective, coming from an unrelated but still technical/mathematically adept discipline (electrical engineering, or quality engineering, or accounting for example)
No sugar coating is necessary. Thank you.



I think that's an excellent question: thank you so much for asking it.

I mentioned this is another answer, but I think the single biggest challenge you'd face is getting screened out before you ever have a chance to speak with someone and prove your competence. As such, I would over-optimize for "How do I get past the resume screen?"

(The tactics I mentioned previously were (a) built out and lean on your network, and (b) create a portfolio (time permitting). However, there are undoubtedly others, whether it's doing a "stepping stone" job for a year or two, pursuing additional education, etc.)

Once you're past the screen, I think it's always easier to tell a compelling story. It's possible to put a career break into the proper context, and to explain why you're making the transition. Additionally, it gives you the chance to tell a compelling narrative about how your past experience (e.g., electrical engineering) makes you uniquely valuable for this role. (Note: There's always a way to spin why you're past experience is uniquely valuable for the role you're interviewing for 😉 )

A stray thought: Unfortunately, hiring managers are all too human, and bring with them a host of biases and misperceptions you'll need to overcome. For example, a hiring manager could look at an experienced "transitioner" and think, "Ugh, are they really going to be 'coachable', or are they stuck in their ways?" This is decidedly not a fair thing for them to think, but even so, it may make sense to address their concerns head-on. A potential story could be, "Hey, I know this is a new career for me, so I'm thinking of myself as a new grad, ready to learn." (Note: I'm not positive if this is the best angle, but something like it might work.)*

In any case, I hope this was helpful. Speaking as honestly as I can, I know I probably didn't do your question the justice it deserves. It's a critically important issue, and one that I think the technology industry should do a much better job of addressing.

*I should also add: I would defer to the career coaching resources that are out there on this topic, as I do not consider myself an expert here 🙂
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Thank you for inviting questions and sharing your insights with the Coursera data science community, @Daniel Saber. We appreciate the time and thought you put into these answers!