• Design, build, and automate Omada’s core enrollment forecasting engine for the existing book of business, significantly reducing manual effort and increasing forecast reliability and reproducibility. • Translate commercial planning questions into scalable forecasting solutions, partnering closely with Commercial Operations, Sales, Marketing, and Finance to ensure the models reflect real-world dynamics and are usable in day-to-day decision making. • Establish and own best practices for model development, backtesting, performance monitoring, and alerting for enrollment forecasts, helping Omada move from one-off analyses to a robust, production-grade forecasting capability. • Improve forecast accuracy and responsiveness over time by continuously experimenting with new data sources, features, and modeling techniques, and systematically incorporating learnings from forecast performance. • Act as the primary technical leader for forecasting within the Data organization, providing guidance on tooling, coding standards, and architecture, and mentoring other data scientists who contribute to forecasting projects. • Free Commercial Operations leadership to focus on product-line strategy and new go-to-market motions by taking ownership of the technical implementation of base forecasting, while collaborating closely on the assumption framework and narrative.

• This role supports the development and maintenance of data-driven and agentic AI solutions, partnering closely with the Managing Data Scientist and cross-functional teams throughout the full development lifecycle. • Responsibilities include contributing to data collection, exploratory analysis, model and agent development, deployment, and ongoing monitoring. • The position focuses on building foundational experience in designing and implementing AI agents and cloud-based AI solutions, while supporting the delivery of scalable systems in production environments. • The individual will collaborate across teams to apply established best practices, leverage reusable components, and help ensure consistent, high-quality execution of AI and analytics solutions.

• Design and own marketing measurement methodologies, including incrementality testing, marketing mix modeling, multi-touch attribution, and experiment design • Build statistical and machine learning models to evaluate channel effectiveness, forecast outcomes, and inform budget allocation • Translate ambiguous business questions into rigorous analytical plans and deliver findings to both technical and non-technical stakeholders

• Lead the development and evolution of the Feature Store capabilities: data lineage, feature views, feature recommendation, and new query engines; • Design and implement Apache Iceberg tables with a focus on read performance, versioning, and schema evolution; • Architect and optimize the serving layer with Redis for real-time features meeting strict latency SLOs; • Integrate and optimize Amazon EMR as a query and large-scale processing engine; • Define and implement feature selection and transformation pipelines with end-to-end traceability; • Establish quality, versioning, and governance standards for features across the platform; • Serve as the technical reference for data and data science teams that consume the Feature Store.