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, and December 2022 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: 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:

  1. Determine how you learn best:

    • self-paced: through books/videos/blogs?

    • instructor-based: through college/university/bootcamp? If so, please see my tips for continuing education here.

    • application-based: by developing your first, proof-of-concept data-centric project at your current job, or on your own?

  2. 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?

    • e.g. Would you prefer roles that are more internal/external client facing than technical? Or vice versa?

    • e.g. If you're looking for an Analyst role, would you prefer to be a BI Analyst, Data Analyst, Product Analyst or an Analytics Engineer? Or other (less mainstream) roles entirely?

  3. Determine what industry/industries you want to get into.

  4. Research (ideal) job postings and figure out what (if any) skill gaps you have.

  5. Decide on what business question in industry from Step (3) you'd like to answer.

  6. (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 can this as 20% time in your current role, to try out proof of concepts that use data to bring value to your current company.

    • 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:

  1. Tailor your resume to the role

  2. Then, in an interview, your portfolio/demo will give you an edge by showcasing your communication skills, business knowledge, and technical expertise.

  3. 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.

  4. 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.

  5. Avoid 7 mistakes you might be making before your job interview even happens

If you need additional support, please check out:

Good luck!

Keywords: Data Science careers, breaking into data, data products

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