Decoding Data Science Job Descriptions

It seems like every company wants to hire Data Scientists these days and Data Science is such a broad field that it is not clear from most job descriptions what the role they need is. It is also not clear if they know what role they need or want what they need. It can be really frustrating getting a job only to discover it has nothing to do with what you expected. There are though so many things one can read between the lines, and from that get some idea about how realistic is the job description, how advanced their data infrastructure, and most importantly to see how diverse and inclusive their culture is. In this talk, I will present some of my tricks to analyze companies and jobs. And this is relevant both for people looking for new roles in Data Science and for companies that strive to do better with their job descriptions and culture.

Presented at https://hopin.com/events/connect-forward-2021

References from last slide
https://hbr.org/2021/02/why-is-it-so-hard-to-become-a-data-driven-company
https://www.themuse.com/advice/not-a-culture-fit-at-current-job
https://textio.com/blog/how-to-craft-a-sincere-equal-opportunity-employer-statement/28880187459
https://talentfoot.com/2020/08/04/gender-neutral-job-descriptions/
https://hbr.org/2014/08/why-women-dont-apply-for-jobs-unless-theyre-100-qualified
https://www.linkedin.com/business/talent/blog/talent-acquisition/how-women-find-jobs-gender-report
https://www.linkedin.com/business/talent/blog/talent-strategy/highly-effective-ways-to-eliminate-hiring-bias
https://builtin.com/job-descriptions/how-to-write-a-job-description
https://www.lifeworthlovingcoaching.com

Transcript

1. DECODING DATA SCIENCE JOB DESCRIPTIONS
2. Co-organizer of PyLadies Hamburg Board member of Python Software Verband D&I and CoC of Python Software Foundation Art: tiyepyep Past: data science lead/manager, data scientist, data engineer .. TEREZA IOFCIU HEAD COACH DATA SCIENCE @NEUEFISCHE
3. HOW IT STARTS Lots of people want to work as data scientist Many jobs are open and companies are looking for all sorts of data scientists TEREZA IOFCIU
4. HOW IT ENDS Most of the times there is a huge mismatch between expectations and reality Both for companies and employees TEREZA IOFCIU
5. GOAL MISSMATCH PEOPLE LOOKING FOR JOBS VS COMPANIES HIRING
6. JOB SEEKERS WANT Understand what role they will have, where (culture) and with whom (team) will they spend a lot of their time with
7. MANY COMPANIES WANT Find out with minimal e ff ort if a candidate f its the job Are you smart? Are you compatible to the culture?
8. FINDING THE DATA SCIENCE DREAM JOB
9. EVALUATING JOBS Job Descriptions Interviews On the Job
10. Takes least time investment to assess JOB DESCRIPTION STAGE Doing research at this stage will only help in the future! Research: job description, company site, linkedin employees, blogs, meetups Think of missing information: WHY is it missing? Ignorance vs Indi ff erence: perfect questions to ask in interview
11. WHAT CAN WE FIND OUT? JOB DESCRIPTION ROLE CULTURE TEAM
12. YOU WILL SPEND ~30% OF YOUR TIME WITH YOUR TEAM TEAM
13. TEAM Team not mentioned.. Team mentioned as in progress Team well described
14. TEAM Team pro f iles online How diverse are they What di ff erent roles do they have Do they inspire you (blogs, open source, meetups, conferences..)
15. TEAM Are there any juniors? How is mentoring happening?
16. IN AN IDEAL WORLD COMPANIES HIRE PEOPLE THEY NEED NOT PEOPLE THEY “WANT” ROLE
17. ROLE Some bullet points Many bullet poitns Too many bullet points -> maturity of the team
18. 24% OF SURVEY RESPONDENTS SAID THAT THEY THOUGHT THEIR ORGANIZATION WAS DATA- DRIVEN meanwhile in 85 Fortune 1000 companies BIG DATA AND AI EXECUTIVE SURVEY 2021
20. BEFORE DATA Data is the new oil You are our f irst data scientist Data science is magic Immediate results All our problems solved Decisions made based on gut feeling No support from upper mgmt for going data driven Reporting and data f lows are still at a manual level TEREZA IOFCIU
21. BEFORE DATA Assisting with reporting or doing all the reporting Convincing that data needs to be tracked, collected and analysed Most e ff ort will be spent on politics Company needs are: business intelligence and data engineering TEREZA IOFCIU
22. LIKING DATA We want to be data driven Data science is still magic Immediate results and on demand improvements Lack of company wide data culture Some decisions based on data insights Solid data infrastructure TEREZA IOFCIU
23. LIKING DATA Convincing people that decisions should be backed by data Do analysis and modelling, though many models will not make it live Lots of e ff ort spent on politics and educating others Lessons learned in prioritising of work Company needs: data literacy training TEREZA IOFCIU
24. 25% DECISION MAKERS ARE DATA LITERATE
25. DATA DRIVEN We publish research Data is at the core of the product Complex problems should be solved by data science Data is in every product/team Data literacy in over 50% of the company Decisions based on data insights Open source, research, publications TEREZA IOFCIU
26. DATA DRIVEN Building data products with the team Advancing the state of the art of research Advocating for data science / your team / product You will be doing data science and more TEREZA IOFCIU
27. LACK OF CULTURE FIT LEADS TO STRESS AND CAN TAKE A LOT OF YOUR WORK TIME TO DEAL WITH CULTURE
28. CULTURE Competitive vs collaborative Volunteering Flexibility
29. CULTURE Inclusive language Gender coding Ableism Ageism Listings with gender neutral wording get 42% more responses
30. CULTURE The lists of skills and requirements... are all marked as important? Too many bullet points -> rigid hiring process and women will be less likely to apply
31. CULTURE Performance objectives Even if they are missing you can turn the skill list into performance objectives and explain why you are quali f ied Where would you apply? a PhD plus 3 years experience Improve the performance of 3 of our production ML models in the f irst 6 months
32. CULTURE Equal Employer Opportunity Statement Does it exists? Is it generic? Jobs with EEO f ill 6% faster
33. Expect to see it 74 % Compensation 61 % PEOPLE WANT TO SEE IT.. COMPANIES HIDE IT THE SALARY Show it 1 % Don't 99 %
34. JOB DESCRIPTION Ignorance vs Indi ff erence You need to decide which one are you willing to deal with What is important for you?
35. WHY DO WE NEED TO DO THIS? DECODING JOB DESCRIPTIONS IS JUST DEALING WITH SYMPTOMS OF A BROKEN SYSTEM
36. MARY BAJOREK (LIFEWORTHLOVING COACHING) WE ARE STILL INTERVIEWING FOR FILLING IN CHAIRS RATHER THAN INTERVIEWING PEOPLE TO PARTICIPATE IN A TEAM AND ACHIEVE COMPANY GOALS
37. TEREZA IOFCIU @TEREZAIF
38. https://hbr.org/2021/02/why-is-it-so-hard-to-become-a-data-driven-company https://www.themuse.com/advice/not-a-culture- f it-at-current-job https://textio.com/blog/how-to-craft-a-sincere-equal-opportunity-employer- statement/28880187459 https://talentfoot.com/2020/08/04/gender-neutral-job-descriptions/ https://hbr.org/2014/08/why-women-dont-apply-for-jobs-unless-theyre-100- quali f ied https://www.linkedin.com/business/talent/blog/talent-acquisition/how-women- f ind-jobs-gender-report https://www.linkedin.com/business/talent/blog/talent-strategy/highly-e ff ective- ways-to-eliminate-hiring-bias https://builtin.com/job-descriptions/how-to-write-a-job-description https://www.lifeworthlovingcoaching.com REFERENCES