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Q&A with a Data Science Hiring Manager

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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  • 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?
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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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- 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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  • 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?
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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.
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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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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?
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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
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
  • 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?
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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.
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.
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
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.
  • 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?
Is is possible a business analyst/project manager can take up the role of data science manager? If so, then what are basic requirements you look into a manager without a Data science background.
  • How can a software engineer working on databases and programming on a daily basis prepare for a career in data science ?
  • What are some platforms(apart from kaggle) where we can showcase our data science interest and personal work to data science hiring managers ?
Hi. I am currently studying post grad Data Science and have had some of the Coursera courses related to it. But before deciding to explore Data Science, I was a software developer for a number of years.

Questions:
  • What is the best way to marry and leverage both Data Science skills and Software Development skills in the current job market?
  • Talking with a number of employers at career fairs, they either just want a developer OR a data scientist. Are there opportunities in the job market that builds on both of the skills or is it usually just one or the other?
Q1: How would you draw the boundary between Data Analyst, Data Engineer and Data Scientist roles?
Q2: Software development has methodologies like Agile, Scrumm and etc. They are not easily applicable to Data Science because of blurry timeframe. What do you employ?
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?
Q1 : What platforms and tools does the coursera Data Science team use to build and deploy models ?

Q2 : Do you have a prize such as 'Data Science' Oscar for students who have completed maximum count of courses from various universities related to Data Science on your platform?

Q3 : What's the maximum amount of time spent on the platform and the count of courses completed by a single coursera student ?
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Liz wrote:

  • 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 🙂
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M_Mahrous wrote:

  • 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 ?



Thank you for the thoughtful questions!

While I don't know much about specific companies offering internships for international students, I'm happy to take a stab at the other two questions:

For a BSc student who is looking for a summer internship, what skills are targeted?



I would lean on the answer I gave to Liz above. In general, we'd be looking for a bit of programming, a bit of statistics, and a bit of analytical intuition. More concretely, our interns are usually going to be solving some real problem (e.g., "build this model," "do this analysis"), and so the most important thing is the ability to generate value from a real data set.

(Thus, one simple way of figuring out if you have the requisite skill set: practice! Think of a problem you're curious find about, find a data set, and see if you have the skills necessary to solve it. When you run into issues, use those issues to guide your learning.)

Any general advice from an expert's point of view for students ?



The fact that you're proactively thinking about your Data Science career already is a huge leg-up -- take advantage!

Whereas many of your peers are going to leave school with purely theoretical knowledge that they'll then need to "transform" into practical knowledge, you can start building your practical knowledge now (e.g., by applying the concepts you learn in class to real-world analysis problems. Side note: this is how I "really" learned econometrics).

Similarly, while many of your peers will graduate and then think, "Uh oh, I need to find a job," you can lay a foundation now that will make your future job hunt easier. In particular, I would focus on building up a network. As a student, one of your biggest advantages is that people actively want to help you 🙂 Thus, by reaching out to enough people and asking them to talk about their experiences, you'll develop (a) a support system you can lean on as you transition from school to industry; and (b) insider knowledge of how Data Science "really works" that will no doubt set you apart in interviews.
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rinnyssance wrote:

- 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?



Thank you for the great questions! I don't know much about Genomic Data Science, so I don't want to speak beyond my ken. However, I'll take a stab at the others :)

- What obstacles do non-degreed data scientists face?



Unfortunately, many companies are going to use degrees as a filter when screening resumes. As such, the single biggest obstacle you would face as a non-degreed Data Scientist is getting the chance to prove your skills in the first place.

Thus, it could be a good idea to pursue "alternative" ways of getting noticed. For example, it might be helpful to build out a portfolio/GitHub of projects that demonstrate your skills. Or perhaps it would be a good idea to build up a network (as I recommend above) whose referrals would let you "sidestep" the resume screen in the first place.

- How feasible is remote or freelance work?



I don't know much about this topic, but I did read a great blog post on this the other month! (link)

The post even links to an awesome set of companies that support remote work!

- What kind of experience can someone get in order to beef up their salary?



I think my high-level advice here is that there are *many* ways to become more valuable in the market. As such, I would focus on growing in the ways that interest you. (If you're not interested in the dimensions in which you're growing, then it'll be hard to remain motivated -- even if you're earning a higher salary.)

That said, to make my answer more concrete, I would lean on what I wrote to Liz above. Once you've built up the base of your Data Science 'T', think about how to develop "unique differentiators." Some of these are probably obvious, e.g., gaining management experience, learning how to put Machine Learning models in production, working on initiatives where you can be the technical lead, etc.

However, to illustrate my point about there being many ways to grow, I would say the following: Ultimately, salary is a function of whether a specific company values what you, specifically, bring to the table. If a particular company has a unique need for NLP skills, then developing those skills could be a good way of boosting your salary. For another company, NLP skills might be a luxury, and so pursuing those skills wouldn't do much for you.

I think this lens -- "how do my unique set of skills increase my value to a specific set of companies bidding for my services?" -- is probably a useful way to think about skill development when optimizing for salary.

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