Sr. Manager, Data Science

• Stakeholder Engagement: Build strong partnerships with AI Product stakeholders, Product Management, Engineering, and business teams to identify business challenges, opportunities, and define requirements for AI solutions
• AI Model Development Roadmap: Lead and continually refine the AI model development roadmap, ensuring alignment between technical strategy, business goals, and emerging opportunities
• Risk Mitigation: Proactively identify and address AI-related risks, maintaining robust standards for model governance, compliance, and ethical deployment of AI solutions
• Model Development Enablement: Anticipate and overcome challenges in data science projects by bridging gaps in technology, data, platforms, or skillsets to ensure teams have the resources and support needed for timely product delivery
• Standardization of Experimentation: Establish and implement standardized processes for experimentation, including documentation, validation, and performance evaluation to ensure consistent, reproducible results
• Technical Review and Guidance: Participate in code and model reviews, providing technical guidance to ensure model robustness and quality while identifying areas for improvement
• Collaboration with Engineering: Partner with engineering teams to ensure smooth integration, scalability, and monitoring of AI models in production environments
• Opportunity Analysis: Help stakeholders pinpoint key problem areas where AI solutions can deliver measurable impact, leveraging organizational data and advanced analytics
• Team Leadership and Development: Mentor and empower a team of data scientists, fostering innovation and aligning technical skills with long-term AI strategy for impactful model delivery
• Continuous Technical Strategization: Monitor industry and academic breakthroughs in AI to proactively set and evolve the technical roadmap, ensuring the team is prepared to deliver state-of-the-art solutions

• 8+ years of industry experience in Data Science, Machine Learning Engineering, or related fields
• Master’s or PhD in Data Science, Machine Learning, Computer Science, Operations Research, Applied Statistics, Biomedical Informatics, or closely related disciplines
• Proven experience managing and leading data science or AI teams, including mentoring junior members and developing team processes
• Experience in productionization of AI models and collaboration with engineering for deployment and support
• Excellent communication and stakeholder management skills
• Proven ability to work across cross-functional teams and drive consensus on technical strategies and business priorities
• Demonstrated expertise in model development, deployment, and performance evaluation for production-grade AI solutions
• Strong foundation in probability, statistics, computer science, time series analysis, linear algebra, and discrete math
• Extensive, practical experience applying advanced AI and machine learning methods—such as NLP, time-series analysis, computer vision, recommender systems, and reinforcement learning—using common algorithms, with a strong understanding of their real-world trade-offs in production settings
• Extensive experience building and managing ETL pipelines and data workflows to handle large, complex structured and unstructured datasets, enabling their effective use in AI algorithm development
• Previous hands-on experience in Python with a strong track record of developing, deploying, and monitoring machine learning models in cloud-based production environments, emphasizing robust evaluation and operational effectiveness

comprehensive medical, vision, dental, and life, AD&D, short-term and long-term disability insurance, sleep care management, Health Savings Account (HSA), Flexible Spending Account (FSA), commuter benefits, 401(k), Employee Stock Purchase Plan (ESPP), Employee Assistance Program (EAP), tuition assistance, Flexible Time Off (FTO), 11 paid holidays plus 3 floating days, eligible for 14 weeks of primary caregiver or two weeks of secondary caregiver leave