Data Science Manager, Top Hat

• Leadership Experience: 2-4 years managing data scientists or ML engineers; 6-8 years in data science, ML, or applied statistics
• Technical Depth: Expertise in Python, SQL, ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch), and modern cloud data platforms (BigQuery, Spark, etc.). Practical experience with LLM-based applications and agent frameworks (Google ADK, LangChain, etc.)
• Applied ML/AI: Hands-on experience delivering personalization, recommender systems, or predictive analytics in production
• Business & Product Sense: Ability to translate ambiguous problems into measurable objectives and practical solutions
• Experimentation Skills: Strong grasp of causal inference, A/B testing, and evidence-based impact measurement
• Excellent Communication: Proven skill in presenting technical and non-technical stories to executives, educators, and cross-functional peers
• Passion for education technology and improving student outcomes

Desirable

• We are seeking a Manager, Data Science to lead a team of talented data scientists in developing evidence-driven insights, predictive models, and AI-powered personalization features that directly impact millions of students and thousands of professors
• Delivering AI and analytics solutions that improve learning outcomes (e.g., withdrawal-rate reduction, mastery-based personalization, agentic AI)
• Driving the next generation of personalization across our products (Ace Practice, Study Planner, Recommendations, etc.)
• Building a strong partnership with Product Development, and Learning teams to ship data-driven features at scale
• Cultivating a culture of curiosity, rigor, and measurable impact within your team
• Lead & Develop the Team: Coach, mentor, and grow a team of data scientists. Foster technical excellence and career development
• Advance AI-First Capabilities: Contribute to the design of agentic AI (using Google ADK, Vertex AI), adaptive mastery algorithms, and graph-based knowledge representations
• Deliver Impactful Models & Insights: Drive roadmap-aligned projects such as learning mastery diagnostics, recommender systems, and A/B experimentation
• Partner Cross-Functionally: Collaborate with Product, Engineering, Learning Materials, and Customer Success to translate educational goals into actionable AI solutions
• Ensure Quality & Reliability: Establish standards for experimentation, model governance, bias detection, and reproducibility
• Tell the Story with Data: Communicate complex technical concepts and evidence-of-impact to leadership and stakeholders