• Own and drive end-to-end ML projects that directly impact product growth, engagement, and monetization, from problem framing to deployment and measurement • Build and maintain ML models such as user segmentation, churn prediction, and recommendation systems to surface actionable opportunities for the product • Partner with Product Managers to translate business questions into well-defined analytical or modeling problems with clear success metrics • Conduct deep-dive analysis on user behavior, funnels, cohorts, and journeys to identify growth opportunities and inform product strategy • Design, execute, and analyze A/B and multivariate experiments with a strong focus on revenue, retention, and engagement impact • Develop and maintain key product KPIs, dashboards, and monitoring metrics to track model performance and product outcomes • Ensure accurate event tracking and data instrumentation for new and existing product features • Translate complex analytical and modeling findings into clear, actionable recommendations for both technical and non-technical stakeholders • Act as a thought partner and data leader within product teams, fostering a strong data-driven and experimentation culture

• Serve as the embedded data science partner to Marketing, bringing rigorous, data-driven decision-making to a team that today lacks dedicated analytical support. • Stand up and own the marketing measurement foundation — event tracking architecture, unified attribution windows, and KPI frameworks across mobile (MMP) and web (GA) — so channels can be compared apples-to-apples. • Build and maintain the core models that drive spend decisions: LTV, ROAS, CAC/payback, and retention; translate them into channel strategy and budget allocation recommendations. • Design, run, and read out experiments and A/B tests across acquisition and the customer lifecycle, and set the experimentation standard for the marketing org. • Analyze the full funnel (acquisition → activation → engagement → retention → monetization), surface the highest-impact growth opportunities, and size them. • Build forecasting and opportunity-sizing models to inform planning and resource allocation. • Develop self-serve dashboards and recurring reporting that give Marketing and leadership clear visibility into performance. • Partner with Data Engineering and MMP/ad-platform teams to ensure data quality and measurement accuracy, and prioritize fixes. • Translate analysis into clear, persuasive recommendations, and influence marketing investment decisions with stakeholders up to leadership.

• Own and drive end-to-end product analytics for key consumer-facing products and initiatives. • Conduct deep-dive analysis on user behavior, funnels, cohorts, and journeys to identify opportunities for growth, monetization, and improved user experience. • Design, execute, and analyze A/B and multivariate experiments to evaluate product, UX, pricing, and growth initiatives. • Partner with Product Managers to define clear hypotheses, success metrics, and guardrails for experiments and product launches. • Translate complex analytical findings into clear, actionable recommendations for technical and non-technical stakeholders. • Develop and maintain key product KPIs, dashboards, and monitoring metrics to track product performance and experiment results. • Ensure accurate event tracking and data instrumentation for new and existing product features. • Leverage AI-assisted tools and ML techniques (e.g., segmentation, propensity scoring) to take on more advanced analysis to support product decisions. • Support ad-hoc data requests from cross-functional teams across the company. • Support strategic deep dives that influence roadmap prioritization and business strategy.

• Research and develop data science methods that improve NEXUS predictive performance across diverse enterprise datasets, industries, and prediction task types • Design and implement robust, production-quality Python components with a strong focus on correctness, generality, and reusability • Deeply understand the characteristics of real-world enterprise data and develop strategies that help NEXUS handle them reliably • Run rigorous experiments to measure the impact of new approaches, design meaningful benchmarks, and use results to guide prioritization • Work across a wide variety of structured data problems - including but not limited to classification, regression, ranking, and forecasting • Collaborate closely with the Engineering and Research teams to develop a deep understanding of NEXUS model behavior and use that knowledge to inform your work • Work with Applied AI Engineers to validate approaches on real customer datasets and translate findings into product capabilities • Contribute to technical documentation and internal best practices, helping the broader team apply new capabilities correctly and confidently