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Science to Data Science. The workshop. 10,000 Data Scientists for Europe.

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At some point, most PhDs wonder whether to continue in academia or leave for a job in the industry. More money, time or impact: Motives will vary, and are equally valid. Data analytics, data science & artificial intelligence constitute the fastest growing job market for the very highly qualified.
Workshop outcome:
1. Guidance: Pros and cons of continuing with an academic career or switching to the industry
2. If switching, a customizable roadmap for completing the career transition in 6 to 9 months
3. An outlook on the industry and startup labor market

These slides were done for the 10,000 Data Scientists for Europe 1st anniversary workshop @ #MPSAECR 2018
More info: datascientist.eventbrite.com

Published in: Science
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Science to Data Science. The workshop. 10,000 Data Scientists for Europe.

  1. 1. Science to Data Science. The workshop.
  2. 2. Expected outcome 2 1. Guidance: Pros and cons of continuing with an academic career or switching to the industry 2. If switching, a customizable roadmap for completing the career transition in 6 to 9 months 3. An outlook on the industry and startup labor market
  3. 3. The workshop 3 Postdocor Industry? TRACK RECORD V CAREER SWITCHING Switchtoindustry ARRIVING IN INDUSTRY OR STARTUP Career EMPLOYMENT & TRENDS
  4. 4. 4 § Track continuity versus career switching: Pro and Contra § Data Science: On impact, reproducibility, and transparency Postdocor industry?
  5. 5. 5
  6. 6. 6 PRO academic career PRO industry switch CONTRA academic career CONTRA industry switch
  7. 7. impact: startup > science 7
  8. 8. reproducibility: software > science 8
  9. 9. transparency: science > ai 9
  10. 10. 10 § 6-9 months roadmap Switchto industry
  11. 11. talent empowerment • Any Ph.D. with a numerate background • Prior experience with Python, R, or Matlab • Readiness to transition to data science is very high Data Science Exploration Domain Orientation Training Career start
  12. 12. 93 61 61 44 34 PYTHON R MATLAB C++ SQL I have prior experience with… N = 116
  13. 13. 33% 30% 20% 17% I want my first position in… 6 months 12 months 24 months later
  14. 14. 185 132 129 129 115 ML TRAINING JOB SEARCH INDUSTRY NEEDS TEAM COMPETITIONS AI USES CASES I need support with… N = 257
  15. 15. exploration 15 Test-drive a few online courses or select some relevant training books Find a practitioner in your network to interview Talk to recruiters and recruiting firms Interview alumni of training providers (e.g. bootcamp, online course) that have made the switch Get a sense of your strength and weakness, and what to focus on for your career switch
  16. 16. orientation 16 Understand the Business Case for data science and machine learning Understand where the biggest opportunities are (e.g. high demand, low entry barrier) Select preferred domains for your career entry, e.g. by industry, by type of machine learning Apply to a few suitable career openings and put your profile (e.g. LinkedIn, GitHub) in front of relevant people
  17. 17. training 17 Do a skills gap analysis for your preferred employment destination If any further training is required, consider doing it full-time and with practitioners Practice making the use & business case for a product Practice how industry business cases and interviews may be different and tougher
  18. 18. career start 18 Understand that network contacts, recruiting firms, and direct applications are equally important to get your profile placed directly in front of hiring managers Look at salary surveys for data science and define your expectation, e.g. €50-60k p.a. Make sure you have some criteria to evaluate job offers, e.g. team, growth opportunity, location, and salary
  19. 19. 19
  20. 20. 20 § Data Science scholarships § Industry trends § Startup funding Career
  21. 21. 15,000 scholarships
  22. 22. Women & Data Science scholarship 24
  23. 23. 26
  24. 24. 10,000 Data Scientists for Europe
  25. 25. Please provide feedback Science to Data Science. Take the 12 question quicktap survey here Review us on Facebook Comment on Meetup

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