• Python: Able to efficiently manipulate data, perform data cleaning and preprocessing tasks. Experience with libraries like pandas, NumPy, scikit-learn, etc

• Proficient in Statistical Analysis and Machine Learning: Strong foundation in statistical analysis and machine learning techniques, including regression analysis, clustering, classification, time series forecasting and model monitoring.

• ML Ops: Maintain and retrain productionized ML models, ensuring reliability, performance, and accuracy through drift analysis, metric tracking, alerting, logging, model versioning, CI/CD integration, performance analysis, and collaboration with cross-functional teams

• Optimization (Good to have): Exposure to Objective function, constraints, Gurobi, Or-tools and any type of solvers

• Communication: Adapt communication styles and levels of technical detail based on the audience's background. Demonstrate strong collaborative communication skills, including active listening and articulating ideas clearly.