👉 The decision was close, but the team has decided to keep looking for someone who might have more direct neural net experience.
👉 Honestly, I think the way you communicated your thought process and results was confusing for some people in the room.
👉 He’s needing someone with an image analysis background for data scientist we’re hiring now.
👉 Quite honestly given your questions [about vacation policy] and the fact that you are considering other options, [we] may not be the best choice for you.
These quotes above are some of the reasons I’ve been given for why I wasn’t offered a data science job after interviewing. I’ve been told a variety of other reasons as well: company decided against hiring remotes after interviewing (I’ve heard this at least 3 times), company thought I changed jobs too frequently, company decided it didn’t have necessary data infrastructure in place for data science work. Multiple companies gave no particular reason; some of these were at least kind enough to notify me they weren’t interested. One company hired someone with a Ph.D. from MIT soon after turning me down.
In the last five years, I’ve clearly interviewed for a lot of data science jobs, and I’ve also been turned down for a lot of data science jobs. I’ve spent a good bit of time reflecting on why I wasn’t offered this job or that. Several folks have asked me if I had any advice to share on the experience, and I hope to offer that here.
You never really know
I learned with graduate school applications years ago: you rarely truly know why you were turned down. Maybe my GRE scores weren’t high enough, or maybe the reviewer rushed through my application in the 5 minutes before lunch. Maybe my statement of interest was too weak, or maybe the department needed to accept an alumni’s child.
The same goes for companies. I’m fairly skeptical that the reasons I have been given for why I was passed by are the full story, and I suspect you will rarely (if ever) know the real reasons why you weren’t offered a job. I try to use the reasons I hear as a way to help me refine my skills and better present myself, but I don’t put too much weight in them.
Some advice anyway
That said, here are a few takeaways from interviewing for probably 20 data science jobs since 2012.
- Companies often use interviews as a time to figure out what they’re really looking for. I suspect this rarely intentional. But actually interviewing candidates forces a team to talk through what they’re actually looking for, and they often realize they had differing perspectives prior to the interview.
- Companies where “data science” is a new addition need your help in understanding what data science can do for them. As much as possible, use the interview to sell your vision for what data science can offer at the company, how you’ll get it off the ground, and what the ROI might be.
- Being the wrong fit for what a company needs is not ideal. I’ve come to appreciate a company trying to ensure my abilities align with their needs. You’d hope this was always the case, but I’ve been hired when it wasn’t. That said, I hesitate to say you should always look for this: if you need a job, and someone offers you a job, you should feel free to take it!
- Data infrastructure is important and many companies are lacking it. Many data scientists can attest to being hired at a company only to discover the data they needed wasn’t available, and they spent months or years building the tools required for them to start their analysis. Many companies are naive about how much engineering effort is required for effective data science. Don’t assume that a company with a grand vision for data science necessarily knows what it will take to accomplish that vision.
- Many companies are still uneasy about data science being done remotely. I think this is silly, but I’m biased.
- There’s little consistency as to what you might be asked in a data science interview. I’ve been asked about Java design patterns, how to solve combinatorics problems, to describe my favorite machine learning model, to explain the SMO algorithm, my opinions about the TensorFlow API, how I do software testing, to analyze a never-before-seen dataset and prepare a presentation in a 4 hour window, the list goes on. I spent a flight to the west coast reading up on the statistics of A/B testing only to be asked largely soft-skills type questions for an entire interview. I’ve largely given up attempting any special preparation for interviews.
- Networking is still king. Hiring is hard, and interviewing is hard; having a prior relationship with an applicant is attractive and reduces hiring uncertainty. In my own experience, my friendships and connections with the data science community on Twitter has shaped my career. Don’t downplay the benefits of networking.
So how do you get a data science job? I don’t know.
I’ve been unbelievably fortunate to be continuously employed since college, but I’m not sure how to tell you to repeat that. The best I have to offer is to reiterate the conclusion of my recent talk about data science as a career. Learn and know the hard stuff: linear algebra, probability, statistics, machine learning, math modeling, data structures, algorithms, distributed systems, etc. You probably won’t use this knowledge every day in your job, but interviewers love to ask about it anyway.
At the same time, don’t forget about the even harder skills: communication, careful thought, prose writing skill, software writing skill, software engineering, tenacity, Stack Overflow. You will use these every day in your job, and they’ll help you present yourself well in an interview.1
- Trey Causey: What it’s like to be on the data science job market
- Trey Causey: Hiring data scientists
- Erin Shellman: Crushed it! Landing a data science job
- Joel Grus: Fizz Buzz in Tensorflow
- With the exception of Stack Overflow. Using Stack Overflow in an interview is strangely taboo. [return]