agility.io - Data Scientist
Requirements
• 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 • 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
Responsibilities
• 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
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].
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