• Build and maintain predictive models that drive marketing strategy, including player LTV, churn risk, CAC payback, and propensity-to-convert models • Own marketing attribution and incrementality analysis across paid channels (Meta, Google, TikTok, affiliates, influencers, etc.), helping the team understand what's actually driving growth • Quantify the causal impact and long-term business value of promotions, bonuses, and lifecycle campaigns, moving beyond surface-level engagement metrics to measure true incremental retention, monetization, and LTV impact • Apply causal inference techniques (geo experiments, synthetic control, diff-in-diff, CausalImpact, uplift modeling) to evaluate marketing investments where clean A/B tests aren't possible • Partner with growth marketers to design, run, and read out experiments: A/B tests, geo-tests, holdout studies, and creative tests • Develop and maintain dashboards and self-serve reporting that give marketing leaders real-time visibility into channel performance, cohort behavior, promo ROI, and funnel health • Clean, structure, and validate data across our marketing stack (ad platforms, MMP, internal event data, CRM) and partner with data engineering to improve our data models where needed • Translate complex analyses into clear, actionable recommendations for non-technical stakeholders • Continuously look for opportunities to automate, improve, and scale how the marketing team uses data

• 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