Sr. Data Scientist - Commercial

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The Sr.
Data Scientist
- Commercial is responsible for designing, developing, deploying, and optimizing advanced analytics, machine learning, and decision science solutions that drive commercial growth, revenue optimization, and data-driven decision-making across the U.S. Commercial organization. The role applies predictive, prescriptive, and optimization techniques to improve pricing strategy, customer segmentation, sales effectiveness, forecasting accuracy, customer retention, and overall commercial performance.

Working closely with Commercial Leadership, Pricing, Sales, Marketing, Finance, and Business Intelligence teams, this position transforms complex business challenges into scalable analytical solutions and actionable insights. The role supports the full analytics lifecycle, including data acquisition, preparation, feature engineering, model development, validation, deployment, monitoring, governance, and continuous improvement.

The Data Scientist also partners with stakeholders to operate analytical solutions through automation workflows, dashboards, and decision-support tools that enable measurable business impact. Success in this role is demonstrated through improved commercial outcomes, enhanced forecasting capabilities, greater operational efficiency, and increased adoption of data-driven decision-making across the organization.

• Develop, validate, deploy, and optimize predictive, prescriptive, and optimization models that support commercial and strategic business objectives using advanced statistical analysis, machine learning, and AI techniques.
• Build analytical models for forecasting, pricing and elasticity analysis, customer segmentation, propensity modeling, opportunity scoring, resource allocation, and scenario planning to drive data-informed business decisions.
• Perform feature engineering, model selection, training, testing, validation, backtesting, and performance monitoring while evaluating emerging analytical methodologies and technologies to enhance model effectiveness and business value.

• Acquire, integrate, cleanse, and transform data from multiple internal and external sources while designing, developing, and maintaining scalable Dataiku workflows, Python pipelines, and SQL processes leveraging Snowflake and enterprise data platforms.
• Ensure data quality, reproducibility, scalability, and governance across analytical environments by applying version control, development best practices, and collaborative processes that support efficient model development and deployment.

• Support the end-to-end deployment of machine learning and analytical solutions using Dataiku automation, Snowflake processes, and modern MLOps practices while monitoring model performance, accuracy, drift, stability, and business impact through established governance frameworks.
• Coordinate model retraining, optimization, and continuous improvement activities while…