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Decoding Data Science Job Descriptions
TEREZA IOFCIU discusses decoding data science job descriptions. She notes that jobseekers want to understand the role, company culture, and team they would be working with, while many companies focus on minimal effort to assess fit. This often leads to mismatches between expectations. Her goal is to help evaluate job descriptions, interviews, and on-the-job experiences to find the ideal data science role and company culture fit. She provides tips on assessing team descriptions, roles, culture and inclusion in job postings.
Decoding Data Science Job Descriptions
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- 2. Co-organizer of PyLadiesHamburg 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 .. TEREZAIOFCIU HEAD COACH DATA SCIENCE @NEUEFISCHE
- 3. HOWITSTARTS Lots of peoplewant to work as data scientist Many jobs are open and companies are looking for all sorts of data scientists TEREZA IOFCIU
- 4. HOWITENDS Most of thetimes there is a huge mismatch between expectations and reality Both for companies and employees TEREZA IOFCIU
- 5. GOAL MISSMATCH PEOPLE LOOKING FORJOBS VS COMPANIES HIRING
- 6. JOBSEEKERSWANT Understand what rolethey will have, where (culture) and with whom (team) will they spend a lot of their time with
- 7. MANYCOMPANIESWANT Find out withminimal e ff ort if a candidate f its the job Are you smart? Are you compatible to the culture?
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- 10. Takes least timeinvestment to assess JOBDESCRIPTIONSTAGE 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 WEFIND OUT? JOBDESCRIPTION ROLE CULTURE TEAM
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- 13. TEAM Team not mentioned.. Teammentioned as in progress Team well described
- 14. TEAM Team pro f iles online Howdiverse are they What di ff erent roles do they have Do they inspire you (blogs, open source, meetups, conferences..)
- 15. TEAM Are there anyjuniors? How is mentoring happening?
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- 17. ROLE Some bullet points Manybullet poitns Too many bullet points -> maturity of the team
- 18. 24%OFSURVEYRESPONDENTSSAIDTHATTHEY THOUGHTTHEIRORGANIZATIONWASDATA- DRIVEN meanwhile in 85Fortune 1000 companies BIG DATA AND AI EXECUTIVE SURVEY 2021
- 20. BEFOREDATA Data is thenew 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. BEFOREDATA Assisting with reportingor 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. LIKINGDATA We want tobe 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. LIKINGDATA Convincing people thatdecisions 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
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- 25. DATADRIVEN We publish research Datais 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. DATADRIVEN Building data productswith 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
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- 29. CULTURE Inclusive language Gender coding Ableism Ageism Listingswith gender neutral wording get 42% more responses
- 30. CULTURE The lists ofskills 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 ifthey 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 Doesit exists? Is it generic? Jobs with EEO f ill 6% faster
- 33. Expect to seeit 74 % Compensation 61 % PEOPLE WANT TO SEE IT.. COMPANIES HIDE IT THESALARY Show it 1 % Don't 99 %
- 34. JOBDESCRIPTION Ignorance vs Indi ff erence Youneed to decide which one are you willing to deal with What is important for you?
- 35. WHYDOWE NEEDTODO THIS? DECODING JOB DESCRIPTIONSIS JUST DEALING WITH SYMPTOMS OF A BROKEN SYSTEM
- 36. MARY BAJOREK (LIFEWORTHLOVINGCOACHING) WEARESTILLINTERVIEWINGFORFILLING INCHAIRSRATHERTHANINTERVIEWING PEOPLETOPARTICIPATEINATEAMAND ACHIEVECOMPANYGOALS
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