Key Responsibilities

• Leverage Python for data manipulation, automation, and predictive modeling using libraries such as Pandas, NumPy, scikit-learn, and stats models.
• Conduct advanced statistical analysis and causal inference to evaluate program effectiveness and business impact.
• Design and implement experimentation frameworks and causal models to derive actionable insights.
• Build, optimize, and manage analytical datasets to support scalable and reproducible data science workflows.
• Develop and maintain modeling workflows in Databricks, including notebooks, jobs, and ML pipelines.
• Ensure production-ready and well-documented analytical solutions with a focus on scalability and performance.
• Collaborate with cross-functional teams and customer stakeholders to understand business requirements and translate them into analytical solutions.
• Present insights, methodologies, and results clearly to technical and non-technical stakeholders.

What do we expect?

• Statistical Analysis & Causal Inference : Expertise in advanced statistical methods, experimentation, and causal modeling. Ability to evaluate program performance and derive meaningful insights. 
• Strong foundations in statistical models, should be able to build Propensity score matching, Difference-in- difference model. Experience in ATT, ATE models and potentially exposure to ROI and ICER models
• Python (Hands-on): Proficiency in Python for data manipulation, modeling, and automation using analytical and machine learning libraries such as Pandas, NumPy, scikit-learn, and statsmodels.
• Databricks: Hands-on experience with Databricks notebooks, workflows, and ML pipelines, along with familiarity in Delta Lake, Spark, and collaborative development environments.
• Data Engineering & Modeling Workflow Optimization: Experience in building, optimizing, and managing analytical datasets and ensuring scalable, reproducible, and production-ready workflows.
• Communication & Stakeholder Engagement: Strong ability to interact with customer stakeholders, understand business needs, and translate them into data-driven solutions.
• Spark / PySpark: Experience with distributed data processing frameworks.
• ETL & Data Pipeline Development: Exposure to data integration, data cleaning, and transformation processes.
• MLflow / Model Tracking Tools: Familiarity with experiment tracking, model versioning, and reproducibility frameworks.
• Visualization & Reporting: Ability to create dashboards and reports using Power BI, Tableau, or Python visualization libraries.
• Cloud Platforms: Experience with Azure, AWS, or GCP (preferably Azure in alignment with Databricks environments).