Perform deep exploratory data analysis to identify patterns, anomalies, data quality issues, and signal strength
Conduct end-to-end feature engineering including feature selection, encoding, scaling, transformation, leakage prevention, and feature importance evaluations
Build and tune predictive models using regression, classification, clustering, ensemble methods, and time-series forecasting
Partner with data engineers to define dataset requirements, validate data quality, and ensure pipeline reliability
Participate in developing model training, inference, scoring, and monitoring pipelines using Azure ML and Databricks

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, or related field
2–4 years of hands-on experience designing, building, and deploying ML models in a business environment
Experience working with cloud data platforms or ML infrastructure (Azure preferred)
Proficiency in Python and ML libraries such as scikit-learn, XGBoost, LightGBM, pandas, NumPy, and statsmodels
Solid SQL skills and familiarity with distributed data tools (Spark, Databricks)
Understanding of classical statistics: hypothesis testing, confidence intervals, regression diagnostics, ANOVA, probability theory

Data Scientist

Key skills