• Design, build, and deploy predictive models that improve learner outcomes and product performance, including user behavior prediction, recommendations, ranking, and classification • Develop, own, and continuously improve end-to-end data science workflows, including ongoing model iteration, retraining, and performance monitoring over time • Take ownership of existing and new predictive models, diagnosing performance issues, adapting to data or product changes, and iterating to improve real-world impact over time • Partner closely with product, engineering, research, and leadership to translate ambiguous questions into data science solutions • Work closely with engineering partners to ship, maintain, and iterate on models in production; you will not be expected to build production infrastructure alone • Contribute to AI and model evaluation frameworks, including offline metrics, experimentation, and qualitative analysis • Analyze large, complex datasets to identify patterns, opportunities, and risks across our platforms • Communicate modeling approaches, assumptions, tradeoffs, and results clearly to technical and non-technical stakeholders • Write technical documentation, reports, and presentations that drive product and organizational decisions • Support responsible, ethical, and equitable use of data and AI in education-focused products

• Extract data to build metrics or create/update Tableau dashboards to track success toward company, department, and customer KPIs • Share insights with internal and external customers regarding drivers of success or areas of opportunity • Analyze data in new areas to help prioritize areas of focus • Consult with internal and external customers to develop new dashboards, metrics, KPIs, outcomes evaluation, or ROI • Respond to ad hoc analytic requests from internal and external customers • Understand and use the data warehouse and relational databases • Ensure accurate quality of deliverables by participating in peer review

• Timely delivery of accurate quantitative data and insights • People management for a team of roughly seven data scientists • Mentoring team members on critical thinking and technical skillset • Partnering with Data Engineering teams to build and maintain data products • Continuous development of experimentation and measurement best practices

• Query and analyze large complex datasets • Use data mining, statistics, and machine learning to discover insights • Build predictive models and propose actionable solutions • Support drug discovery and biotherapeutics efforts • Perform quantitative modeling of complex chemical and biological systems • Collaborate with administrative leaders, clinicians and IT specialists • Build machine learning models • Design and implementation of pilots for validation and optimization