• Design, build, and own end-to-end machine learning models and AI-powered solutions, from problem framing through production deployment and impact measurement, focused on driving customer retention and operational excellence • Develop and enhance AI-driven tools that deliver explainable, actionable insights to Customer Success and Renewals teams, enabling data-informed decisions that protect and grow revenue • Partner closely with Customer Success, Renewals, Product, and Engineering to translate complex business challenges into well-scoped data science initiatives aligned to high-impact retention opportunities • Evaluate, prototype, and implement emerging AI/ML approaches, including generative AI and agentic workflows, to improve team efficiency and stakeholder value in a rapidly evolving landscape • Mentor and support fellow Data Scientists by setting high standards for analytical rigor, code quality, and documentation while contributing to best practices and team growth

• 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

• Lead analytics and investigations that help the business understand exposure patterns and accumulation risk • Define and enforce data standards, quality controls, and best practices for exposure data across business lines • Own data quality and completeness - you not only collect feedback, but intuit what needs to be fixed from your industry experience, and collaborate with other departments on permanent solutions to reliably produce best in class exposure data • Lead the oversight into ingestion, transformation, and normalization of exposure data from internal and external sources • Validate and QA exposure data: identify anomalies, gaps, duplicates, inconsistencies, and drive improvements • Partner with actuarial, underwriting, catastrophe modeling, and product teams to understand their exposure needs • Develop and maintain exposure data documentation, data dictionaries, and process guidelines • Enable and support analytical use cases (e.g. accumulation risk, portfolio stress testing, scenario analysis) • Build, monitor and track data quality KPIs, build dashboards or alerts to surface issues proactively • Support ad hoc analysis to diagnose exposure trends and concentration risk • Provide guidance on integrating exposure data into downstream tools (e.g. modeling engines, pricing systems, BI)

• 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.