Where Data Scientist resumes lose out.
And how to fix each gap.
Almost none use the word “owned” to describe their work. We have spent years hiring for AI, data and analytics roles, and these are the gaps we see most in Data Scientist resumes. None of them is about ability, and every one is fixable.
Where Data Scientist resumes lose out
Data science resumes say which model was trained. They rarely say which decision it changed, who acted on it, or how anyone knew it worked. GenAI is now so common that mentioning it no longer sets anyone apart.
The decision, not the model
Almost none
use the word “owned”, and only about 1 in 5 say “led”.
A model nobody acts on is a prototype. Most resumes say what was trained and stop. The parts that show ownership are missing: the decision the model informed, who acted on it, and what happened next.
Built a churn prediction model using XGBoost.
Built the churn model and owned its rollout: the retention team called the top-scoring 10% for three months, and churn in that group fell against a holdout that was left alone.
An illustrative example.
Ask yourself
- What decision did this model change, and who made it?
- What did people do differently because of it?
- How did you know it worked: a holdout, an A/B test, a before and after?
- What happened after launch: drift, retraining, who maintained it?
GenAI is now the norm
2 in 3
mention GenAI, LLM or RAG work, and about 6 in 10 name RAG specifically.
When most resumes mention the same thing, mentioning it stops helping. What separates candidates is what they built with it, how they evaluated it and what went wrong along the way.
Worked on LLM and RAG based applications using LangChain.
Built retrieval over support tickets and wrote the evaluation set that showed it answered 4 in 10 questions wrongly. Changing how documents were split moved that to 1 in 10.
An illustrative example.
Say what sits behind the buzzword
- What problem was retrieval or prompting solving, and for whom?
- How did you evaluate it: a test set, human review, which metrics?
- What failed, and what did you change?
- What did it cost, and how fast was it?
Results without a baseline
Most
of the improvements resumes claim are round figures ending in 0 or 5. If they were measured, you would expect only about 1 in 5.
“Improved accuracy by 25%” means little without a baseline. Compared with what, on which data, and did it change a decision? A number without those answers is easy to doubt and impossible to check.
Improved model accuracy by 25%.
Raised recall on the fraud class from 0.62 to 0.81 on a held-out month of data, against the previous rules-based baseline.
An illustrative example.
Make a result checkable
- State the baseline it beat.
- Say which metric you used, and why that one.
- Name the data: how much, which period, and whether it was held out.
- Say what the improvement was worth: money, time or decisions.
Also worth fixing
1 in 4
link a GitHub profile
The rest describe their work with nothing to check. A public notebook or write-up, with private data removed, lets a reader see how you think.
Nearly 9 in 10
list scikit-learn, and almost as many list Python
The toolkit list cannot tell one candidate from another. Say what you did with a tool, and why you chose it over the obvious alternative.
Figures come from a small set of 28 Data Scientist resumes, from a few job postings and from candidates who had reached an interview stage, so they are shown as loose proportions. The before and after examples are illustrative, not taken from any candidate.
Keep going
- How to answer “Tell me about a production incident”
- How to put a real number on your data work
- How to show ownership, not just contribution
- How to answer “Walk me through a pipeline you built”
- How to answer “Tell me about a model in production”
- Check one of your resume bullets for free
- Where Data Engineer resumes lose out
- Where AI & ML Engineer resumes lose out
Make your work impossible to overlook.
Start with your resume. graph shows you what it understood and what is missing.