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I manage a data science team and revamped the hiring process pretty substantially about a year ago, to good results. Nothing in here is particularly original, b

I manage a data science team and revamped the hiring process pretty substantially about a year ago, to good results. Nothing in here is particularly original, but here's what we do: 1. Break down "data science" into several different roles–in our case, Analyst (business-oriented), Scientist (stats-heavy), Engineer (software-heavy). Turns out that what we mostly want are Engineers-Analysts, so our process screens heavily for those. 2. Figure out which types of people can be trained to be good at those roles, given the team's current skillset. I opted to look primarily for people with strong analysis skills and some engineering. 3. Design interview tasks/questions that screen for those abilities. In my case, the main thing I did was make sure that the interviews depended very little on pre-existing knowledge, and a lot on resourcefulness/creativity/etc. E.g. the (2-hour) takehome is explicitly designed to be heavily googleable. 4. Develop phone screens that are very good at filtering people quickly, so that we don't waste candidates' time. By the time someone gets to an onsite interview on our team there's something like a 50% chance they'll get an offer. On the candidate side, when I'm applying I try to figure out first and foremost what a company means by "data scientist", usually by networking & talking to someone who already works there. This filters out maybe 90% of jobs with that title, and then I put more serious effort into the rest.

lifeonahilltop 10y ago
Can I ask what are the rough percentages of the three types of data science jobs in the industry? And is there an easy way to search for only one type of jobs (such as using some particular keywords on indeed.com)? I have some advantage only in the scientist/stats-heavy type of data science jobs and would like to better focus my job-searching effort. Thanks.
lukejduncan 10y ago
This is a great breakdown. Data Science can mean a lot of different things and I think the three roles you defined are spot on. At LinkedIn, what used to be called Data Science is now exclusively analyst roles. We have a separate org (hundreds of people) called Relevance which is part of the engineering org and is composed of scientists and engineers. Everyone in that org falls somewhere on the scientist to engineer spectrum with ML / Stats backgrounds and a bias towards ML engineers. Most of the people who interview would consider the relevance roles "Data Science" but the term within the company is still tightly coupled with pure analyst functions.
sheeshkebab 10y ago
I wish all interviewing was like that above - typically it's - create some incoherent job description, run through a bunch of semi-random candidates, pick a random guy that fits the budget, rinse/repeat... Candidates know that, typical in person interview has a 1/10 chance (or worse) of getting an offer from an inperson interview, and so most candidates, including good ones, would care less to spend much time on interviewing with any one employer, or do much to increase their unknown chances. As a rusult employers don't get right candidates and then wine about lack of talent and pay for crappy tools that just sort resumes half assed way... good grief.
Declanomous 10y ago
I've been looking for a job, and I've found how vaguely organizations define their data scientist and analyst roles in their job postings really frustrating. They tend to have a short description of the role, which is generally filled with buzzwords, followed by a list of requirements. I wish organizations would talk about what they wanted to do with their data instead. For instance, a common description might say the candidate will be working with "big data" to help with "data-driven initiatives" and the requirements will be something like "knowledge of Excel, with a Masters in Statistics, or equivalent experience". It's really hard to tailor a cover-letter or a resume to a job posting like that. For one thing, I can't even imagine what kind of work they are doing if they are using Excel for "big data". Second of all, I currently have a job, and writing cover letters and creating resumes takes a lot of time. By the time I get to the phone screen I've probably already spent at least a couple of hours applying. Plus, in the interest of keeping my cover letter and resume short, I have to leave off a fair amount of my experience and performance metrics. Honestly, at this point I think I'm just going to start reaching out to people in the fields I'm interested in and asking them if they know of any roles that would fit my skill-set. The way I see it, I'd at least have a chance of getting feedback from someone who can view my skill-set holistically, rather than HR, who will let me know that I don't tick all their boxes (or vice versa).
- joshvm 10y ago
  > I wish organizations would talk about what they wanted to do with their data instead. It may seem like a daft question, but have you tried asking them? They may clam up and refuse to give you anything, but I would imagine most small companies would be willing to talk about what the role involves. It's a bit late at the end of the interview to be asking "So what would I be doing?". You may find out that they're working on some data which is e.g. image-heavy and you happen to be an image processing expert.
- apohn 10y ago
  >>I've been looking for a job, and I've found how vaguely organizations define their data scientist and analyst roles in their job postings really frustrating. I lead a Data Science team and part of the struggle with writing sensible job descriptions is that there are too many people providing input into the job description. HR can also put their hand in the pot when they try to use buzzwords (e.g. Hadooop) to internally justify why a role with 2 years of experience needs to be paid like other roles (e.g. traditional Excel based Analyst) with 5-10 years of experience. >>They tend to have a short description of the role, which is generally filled with buzzwords, followed by a list of requirements. I wish organizations would talk about what they wanted to do with their data instead. One major challenge for Data Scientists is how hyped the role is, leading to people in an organization believing whatever they want about Data Scientists. Are you a leader who wants a business analyst who can use software and interface with IT? Data Scientist. Are you an engineering manager who wants a person who can interface with the business and use machine learning? Data Scientist. Are you a VP who thinks big data and ML is the problem to your bad or non-existent data? Data Scientist. Do you want somebody who can exhale the maximum amount of hot air while still sounding like a tech and math genius? Data Scientist. Also add in that business people with minimal experience in modern Analytics are trying to build up Data Science and Analytics capabilities in their own part of the organization because they realize Excel is not the answer to every question. I've spent a lot of time speaking with people to help them understand the type of people they need to hire. Sometimes people are sensible and sensible job descriptions and expectations come from that. Other times they are adamant about what they need (even if they are wrong) and the end result are convoluted job descriptions that are either never filled or filled with the wrong person.
nickdavidhaynes 10y ago
Could you talk more about your phone screens? What criteria are you filtering on? What sorts of questions have you found to be effective at stratifying candidates?
- SatvikBeri 10y ago
  I'm mostly looking for people who want to learn about engineering with largeish datasets (~100gb/day for us), and have some of the prerequisite skills. Our codebase is mostly in Spark/Scala and uses functional programming idioms, so I'm looking for people who either know or want to learn how to use those. I'm also specifically trying to filter out people who mostly want a stats-heavy, machine learning heavy job, since that's not what we do. An engineer who wants to learn data science is a great fit for us, an academic who wants to write R all day is not (though an academic who wants to learn engineering/functional programming is fine!) Beyond that, I ask some questions about projects they've worked on, and in particular, how their approach would change if assumptions were different. Here I'm looking for the ability to reason backwards from a business goal, as opposed to somewhat blindly applying statistical techniques. If they do well on these, we send the take-home exam. As previously noted, this is specifically designed to require relatively little knowledge but heavily test analysis skills, and lightly test programming skills. It's almost impossible to complete this exam without using Google effectively, so that's another thing I'm testing.
  - walshemj 10y ago
    can I ask why functional programming in particular I can see why you might want to avoid java for big data - but isn't the average ML algo more in the procedural mould? Would not python with numpy be a better fit ? or fortran with some handwave interface code Back (early 80;s) when I did map reduce we used PL1/G
    - cjhanks 10y ago
      Functional programming for a lot of numerical computing maps easier to mathematical notation. However, Scala is usually a worse choice than Java for numerical computing since everything is a boxed type.

lifeonahilltop 10y ago

Can I ask what are the rough percentages of the three types of data science jobs in the industry? And is there an easy way to search for only one type of jobs (such as using some particular keywords on indeed.com)? I have some advantage only in the scientist/stats-heavy type of data science jobs and would like to better focus my job-searching effort. Thanks.

lukejduncan 10y ago

This is a great breakdown. Data Science can mean a lot of different things and I think the three roles you defined are spot on. At LinkedIn, what used to be called Data Science is now exclusively analyst roles. We have a separate org (hundreds of people) called Relevance which is part of the engineering org and is composed of scientists and engineers. Everyone in that org falls somewhere on the scientist to engineer spectrum with ML / Stats backgrounds and a bias towards ML engineers. Most of the people who interview would consider the relevance roles "Data Science" but the term within the company is still tightly coupled with pure analyst functions.

sheeshkebab 10y ago

I wish all interviewing was like that above - typically it's - create some incoherent job description, run through a bunch of semi-random candidates, pick a random guy that fits the budget, rinse/repeat... Candidates know that, typical in person interview has a 1/10 chance (or worse) of getting an offer from an inperson interview, and so most candidates, including good ones, would care less to spend much time on interviewing with any one employer, or do much to increase their unknown chances. As a rusult employers don't get right candidates and then wine about lack of talent and pay for crappy tools that just sort resumes half assed way... good grief.

Declanomous 10y ago

I've been looking for a job, and I've found how vaguely organizations define their data scientist and analyst roles in their job postings really frustrating. They tend to have a short description of the role, which is generally filled with buzzwords, followed by a list of requirements. I wish organizations would talk about what they wanted to do with their data instead. For instance, a common description might say the candidate will be working with "big data" to help with "data-driven initiatives" and the requirements will be something like "knowledge of Excel, with a Masters in Statistics, or equivalent experience". It's really hard to tailor a cover-letter or a resume to a job posting like that. For one thing, I can't even imagine what kind of work they are doing if they are using Excel for "big data". Second of all, I currently have a job, and writing cover letters and creating resumes takes a lot of time. By the time I get to the phone screen I've probably already spent at least a couple of hours applying. Plus, in the interest of keeping my cover letter and resume short, I have to leave off a fair amount of my experience and performance metrics. Honestly, at this point I think I'm just going to start reaching out to people in the fields I'm interested in and asking them if they know of any roles that would fit my skill-set. The way I see it, I'd at least have a chance of getting feedback from someone who can view my skill-set holistically, rather than HR, who will let me know that I don't tick all their boxes (or vice versa).

joshvm 10y ago
> I wish organizations would talk about what they wanted to do with their data instead. It may seem like a daft question, but have you tried asking them? They may clam up and refuse to give you anything, but I would imagine most small companies would be willing to talk about what the role involves. It's a bit late at the end of the interview to be asking "So what would I be doing?". You may find out that they're working on some data which is e.g. image-heavy and you happen to be an image processing expert.
apohn 10y ago
>>I've been looking for a job, and I've found how vaguely organizations define their data scientist and analyst roles in their job postings really frustrating. I lead a Data Science team and part of the struggle with writing sensible job descriptions is that there are too many people providing input into the job description. HR can also put their hand in the pot when they try to use buzzwords (e.g. Hadooop) to internally justify why a role with 2 years of experience needs to be paid like other roles (e.g. traditional Excel based Analyst) with 5-10 years of experience. >>They tend to have a short description of the role, which is generally filled with buzzwords, followed by a list of requirements. I wish organizations would talk about what they wanted to do with their data instead. One major challenge for Data Scientists is how hyped the role is, leading to people in an organization believing whatever they want about Data Scientists. Are you a leader who wants a business analyst who can use software and interface with IT? Data Scientist. Are you an engineering manager who wants a person who can interface with the business and use machine learning? Data Scientist. Are you a VP who thinks big data and ML is the problem to your bad or non-existent data? Data Scientist. Do you want somebody who can exhale the maximum amount of hot air while still sounding like a tech and math genius? Data Scientist. Also add in that business people with minimal experience in modern Analytics are trying to build up Data Science and Analytics capabilities in their own part of the organization because they realize Excel is not the answer to every question. I've spent a lot of time speaking with people to help them understand the type of people they need to hire. Sometimes people are sensible and sensible job descriptions and expectations come from that. Other times they are adamant about what they need (even if they are wrong) and the end result are convoluted job descriptions that are either never filled or filled with the wrong person.

nickdavidhaynes 10y ago

Could you talk more about your phone screens? What criteria are you filtering on? What sorts of questions have you found to be effective at stratifying candidates?

SatvikBeri 10y ago
I'm mostly looking for people who want to learn about engineering with largeish datasets (~100gb/day for us), and have some of the prerequisite skills. Our codebase is mostly in Spark/Scala and uses functional programming idioms, so I'm looking for people who either know or want to learn how to use those. I'm also specifically trying to filter out people who mostly want a stats-heavy, machine learning heavy job, since that's not what we do. An engineer who wants to learn data science is a great fit for us, an academic who wants to write R all day is not (though an academic who wants to learn engineering/functional programming is fine!) Beyond that, I ask some questions about projects they've worked on, and in particular, how their approach would change if assumptions were different. Here I'm looking for the ability to reason backwards from a business goal, as opposed to somewhat blindly applying statistical techniques. If they do well on these, we send the take-home exam. As previously noted, this is specifically designed to require relatively little knowledge but heavily test analysis skills, and lightly test programming skills. It's almost impossible to complete this exam without using Google effectively, so that's another thing I'm testing.
- walshemj 10y ago
  can I ask why functional programming in particular I can see why you might want to avoid java for big data - but isn't the average ML algo more in the procedural mould? Would not python with numpy be a better fit ? or fortran with some handwave interface code Back (early 80;s) when I did map reduce we used PL1/G
  - cjhanks 10y ago
    Functional programming for a lot of numerical computing maps easier to mathematical notation. However, Scala is usually a worse choice than Java for numerical computing since everything is a boxed type.