Lessons learned from Hiring and Retaining Data Practitioners

Every company wants to hire Data Scientists these days. But what is a Data Scientist? The role is quite broad, Data Science is a team effort, and many job descriptions want everything. It is also not clear if the companies know what role they need or want what they need. It can be frustrating getting a job only to discover it has nothing to do with what you expected. There are so many things one can read between the lines and get some idea about how realistic the job description is. How advanced is the data infrastructure? How diverse and inclusive is the culture? This talk 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 and attract data scientists that fit their needs.

References:
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
https://www2.deloitte.com/content/dam/Deloitte/us/Documents/technology-media-telecommunications/us-tmt-how-do-you-retain-your-data-scientists.pdf
https://www.cnbc.com/2019/12/03/a-tech-firm-tried-it-all-to-stop-turnover-only-one-thing-worked.html
https://towardsdatascience.com/stop-hiring-data-scientists-improve-ml-product-team-retention-8ae04dac406e
https://developerpitstop.com/how-long-do-software-engineers-stay-at-a-job

Transcript

1. HIRING AND RETAINING DATA PRACTITIONERS Mary Jackson at work. NASA Langley Research Center
2. SHE/HER Co-organizer of PyLadies Hamburg Board member of Python Software Verband D&I and CoC of Python Software Foundation DISC Committee Member Art: tiyepyep TEREZA IOFCIU HEAD DATA SCIENCE @NEUEFISCHE
3. HOW IT STARTS Lots of people want to work as data something Many jobs are open and companies are looking for all sorts of data practitioners 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. EVERYBODY WANTS EMPLOYEE RETENTION How long will the company ful f ill the employee's needs (tenure) How often will your colleagues change? (turnover rate)
9. FINDING THE DATA DREAM JOB
10. EVALUATING JOBS Job Descriptions Interviews On the Jobs
11. 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 (current and past), blogs, meetups Think of missing information: WHY is it missing? Ignorance vs Indi ff erence: perfect questions to ask in the interviews
12. WHAT CAN WE FIND OUT? JOB DESCRIPTION ROLE CULTURE TEAM RETENTION
13. YOU WILL SPEND ~30% OF YOUR TIME WITH YOUR TEAM TEAM
14. TEAM Team not mentioned.. Team mentioned as in progress Team well described
15. 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..)
16. TEAM Are there any juniors? How is mentoring happening? How large is the data team?
17. IN AN IDEAL WORLD COMPANIES HIRE PEOPLE THEY NEED NOT PEOPLE THEY “WANT” ROLE
18. ROLE Some bullet points Many bullet points Too many bullet points -> maturity of the team
19. 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
21. 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
22. 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
23. 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
24. 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
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. POLITICS AT WORK Mostly inevitable How long has the company been operating before data came along? TEREZA IOFCIU
28. LACK OF CULTURE FIT LEADS TO STRESS AND CAN TAKE A LOT OF YOUR WORK TIME TO DEAL WITH CULTURE
29. CULTURE Competitive vs collaborative Volunteering Flexibility
30. CULTURE Inclusive language Gender coding Ableism Ageism Listings with gender neutral wording get 42% more responses
31. 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
32. 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
33. CULTURE Equal Employment Opportunity Statement Does it exist? Is it generic? Jobs with EEO f ill 6% faster
34. Expect to see it 74 % Compensation 61 % PEOPLE WANT TO SEE IT.. COMPANIES HIDE IT THE SALARY Show it 1 % Don't 99 %
35. COMPANIES CANNOT ELIMINATE TURNOVER… BUT THEY CAN EMBRACE IT RETENTION
36. IS IT GOING TO BE “BORING”? Using only advanced algorithms … likely to happen in smaller companies stability vs interestingness? TEREZA IOFCIU
37. THE MISSION What is the company's mission? When do you need to be aligned with the mission of the company in your career? paid to learn vs working on meaningful topics TEREZA IOFCIU
38. CAREER TRANSPARENCY How do people get promoted? Is learning part of work? Do people change tracks often internally? Are there career pipelines in place? average performance pay raise 3% vs job change pay raise 14.8% … it takes on average 6-9 months of salary to replace an employee TEREZA IOFCIU
39. RESILIENCE Data teams as silos? Or crossfunctional? How often did team structure change? Who decides? Success = data + science + engineering + product + UX + business strategy TEREZA IOFCIU
40. JOB DESCRIPTION Ignorance vs Indi ff erence You need to decide which one are you willing to deal with What is important for you?
41. ROLE CULTURE TEAM RETENTION
42. WHY DO WE NEED TO DO THIS? DECODING JOB DESCRIPTIONS IS JUST DEALING WITH SYMPTOMS OF A BROKEN SYSTEM
43. 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
44. TEREZA IOFCIU @TEREZAIF
45. 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 https://www2.deloitte.com/content/dam/Deloitte/us/Documents/technology-media- telecommunications/us-tmt-how-do-you-retain-your-data-scientists.pdf https://www.cnbc.com/2019/12/03/a-tech- f irm-tried-it-all-to-stop-turnover-only-one-thing- worked.html https://towardsdatascience.com/stop-hiring-data-scientists-improve-ml-product-team- retention-8ae04dac406e https://developerpitstop.com/how-long-do-software-engineers-stay-at-a-job REFERENCES