Data Scientist

Develop and refine statistical, Bayesian, and machine learning models to support demand forecasting across multiple planning horizons.

Analyze large, complex datasets and build simulation and scenario-analysis capabilities to understand business dynamics involving patients, payers, providers, and market conditions.

Contribute to the entire modeling lifecycle including problem framing, feature engineering, model development, validation, deployment support, monitoring, and explainability, while collaborating cross-functionally and communicating findings to diverse stakeholders.

4+ years of experience applying data science, analytics, or statistical modeling in industry, research, or enterprise environments.

Experience with time-series forecasting, predictive modeling, or related quantitative methods.

Proficiency in Python and SQL; experience with libraries such as scikit-learn, PyMC, PyTorch, or TensorFlow.

Solid analytical and statistical foundation with ability to work with messy and complex datasets.

Experienced in end-to-end forecasting model development with a strong quantitative and statistical background.

Comfortable working with ambiguous problems and multi-disciplinary teams, indicating a strategic and collaborative operating style.

Familiarity with applying forecasting and decision science methods in complex environments, preferably with exposure to causal inference or probabilistic modeling.