[Hiring] Data Scientist @Agility IO

Role Description

We are seeking a Data Scientist to evaluate AI-generated data 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. 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's this role.

• 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
• Build, train, and evaluate machine learning models (classification, regression, clustering, forecasting), including feature engineering, cross-validation, and metric selection
• Document methodology, assumptions, and limitations so that work can be reviewed and reproduced
• Collaborate with engineers, product managers, and other team members, and turn open-ended questions into well-defined analytical problems

Qualifications

• Professional, academic, or serious independent experience doing real data analysis in industry or research
• Proficiency in Python and its data stack (pandas, NumPy, SciPy, scikit-learn, statsmodels, or equivalents) and in SQL
• Experience working in Jupyter or similar notebook environments
• Experience with data visualization tools and libraries (matplotlib, seaborn, Plotly, or BI tools such as Tableau or Power BI)
• Working knowledge of machine learning fundamentals: model selection, overfitting, evaluation metrics, and validation strategies
• Clear written and spoken English
• No degree required

Preferred Qualifications

• Background as a Data Scientist, Data Analyst, or Analytics Engineer
• Experience reviewing, auditing, or red-teaming someone else's analysis or model output
• Familiarity with common pitfalls in applied statistics: p-hacking, leakage, confounding, multiple comparisons
• Familiarity with cloud platforms (AWS, GCP, or Azure), data warehouses (Snowflake, BigQuery, Redshift), and MLOps practices such as experiment tracking and model versioning
• Experience with Git and collaborative, version-controlled workflows
• Prior experience with AI/ML data annotation, evaluation, or model-training projects

What We Look For

• Skepticism and curiosity: you ask whether a result is real before you ask whether it is exciting
• Honesty about uncertainty: you communicate what the data can and cannot support
• Strong written communication and attention to detail
• Self-direction: you are comfortable working independently and asynchronously in a remote environment

Benefits

• Competitive salary and performance-based bonuses
• Flexible remote work environment
• Professional growth opportunities and mentorship
• Engaging and collaborative team culture with cutting-edge projects

Ready to Make an Impact?

If you're passionate about rigorous data work and want to help shape how the next generation of AI models reason about data, we'd love to hear from you — send your resume to [email protected] .