Breaking into your First Data Role: Tips to Tackling the Cold Start Problem
Tips for the Aspiring Data Scientist, Data Analyst, Machine Learning Engineer, Data Engineer or Analytics Engineer
Post was originally published in October 2019 and has been updated in May + September 2020 for relevancy.
Question: What can I do to break into -- or transition careers into -- my first role as a Data Scientist, Data Analyst, Machine Learning Engineer, Data Engineer or Analytics Engineer?
Answer: ...it depends. It mainly depends on your interests, technical and soft skills, and your application strategy.
Advice on how to improve your technical skills also depends... on how you learn best, how much coding is required for the role, how much you're comfortable with, what you're looking to get out of the class/blog, etc. Having said that, here's my step-by-step guide that can help you break in:
Determine how you learn best:
Have the end goal in mind. What would you (ideally) do in the role? What's the (approximate) breakdown of responsibilities by business, technical and statistical expertise that you'd prefer to do?
Determine what industry/industries you want to get into.
Research (ideal) job postings and figure out what (if any) skill gaps you have.
Decide on what business question in industry from Step (3) you'd like to answer.
(Easier said than done) I highly encourage you to develop a demo (project, tutorial, talk, notebook, etc.) -- that answers the key business question in the industry from Step (5) and fills-in 1+ (if any) gaps that you've identified in Step (4).
Bonus if the business question relates to -- or is explicitly mentioned in -- the job opportunities you've identified in Step (4).
Bonus if you're transitioning careers and "can leverage your existing expertise on a domain you understand well", recommends Jason Yamada-Hanff.
Bonus if you add a link to this demo to your resume.
Bonus: if you also follow Rachel Tatman's advice and avoid topics and datasets she mentions for your demo.
Then, as part of your application strategy:
Then, in an interview, your portfolio/demo will give you an edge by showcasing your communication skills, business knowledge, and technical expertise.
Show up (virtual/in-person) -- even if it's once per month -- for meet-ups focused on your ideal industry and technical role(s) (from Steps 3 + 4 above). Please note: these may be different meet-ups.
When you reach out to prospective employers, you do so with a tailored message.
Please note: If you change the company name/industry/etc. and the content is still relevant, then the note is too general.
If you need additional support, please check out:
Leach Teach Code's study groups and monthly mentoring nights;
Women Who Code's meet-ups and workshops;
Schedule a (fee-based) session to "pick my brain" on what next step you can take now to get closer to your next role.
Keywords: Data Science careers, breaking into data, data products
You may also like:
Emerging spectrum of Data and Analytics roles, by Gartner via Laura Ellis
Data Science is Different Now, by Vicky Boykis (Feb 2019)
Getting into Data, by Matt Arderne
Interview Strategy that Landed me my First Data Science job, by Kate Marie Lewis (Feb 2020)
6 Principles to Guide my (Startup) Job Search, by Mansi Kothari (April 2020)
What no One will Tell you about Data Science Job Applications, by Edouard Harris (Feb 2019)
How I Got 4 Data Science Offers and Doubled my Income 2 Months after being Laid Off, by Emma Ding (Sept 2020)
You're more free than you think: Employee liquidity: what it means and what to do, by Product Lessons -- on how to think of your career as an external product
Don’t Focus on Your Job at the Expense of Your Career, by Dorie Clark for Harvard Business Review