Overview
The newest models can spin up a full analysis in minutes — load the data, pick a method, produce charts and a confident conclusion. Someone has to check whether the statistics actually hold up, and that someone is you.
As a Data Scientist you’ll evaluate AI-generated analyses on real datasets, stress-test the reasoning behind them, and write the judgments that teach the next generation of models what rigorous data work looks like.
What you’ll actually do
- Evaluate AI-built analyses on real datasets: the method choice, the assumptions, and whether the conclusion actually follows from the numbers.
- Red-team the statistics to expose leakage, p-hacking, confounded comparisons, and confident nonsense before real users trust them.
- Write the better analysis when the model falls short: sound methodology, honest uncertainty, clear takeaways.
Roles this fits
Common backgrounds: Data Scientist, Data Analyst, Analytics Engineer.
What we look for
- Professional or academic experience doing real data analysis — industry, research, or serious independent work.
- Fluency with the standard toolkit; most tasks use Python, SQL, and notebooks.
- Clear written English: your explanations are the training signal.
- No degree required. We care about what you can do, not where you learned it.