Data Science Manager

Data Science Manager

Requirements

• MS or PhD in a quantitative field (statistics, mathematics, computer science, physics, or related)
• 8+ years of experience in data science and analytics, including 5+ years managing high-performing data teams
• Proven track record of driving product strategy through data insights and building metrics that matter
• Deep expertise in statistical analysis, experimental design, and causal inference
• Experience with SQL, Python, R, and data visualization tools for large-scale analysis
• Demonstrated ability to partner effectively with cross-functional stakeholders and influence decision-making through data
• Experience building data capabilities from early stages through rapid growth phases
• Strong communication skills, with ability to translate complex technical concepts to diverse audiences
• Passion for Anthropic’s mission of building safe and beneficial AI

Responsibilities

• Lead and develop a high-performing team of data scientists, providing mentorship, guidance, and career development opportunities
• Partner with Sales, Marketing, Compute, and Finance leadership to develop metrics, dashboards, and analyses that inform strategic decisions across these domains
• Drive product strategy through data-driven insights, including user behavior analysis, product performance metrics, causal inference and experimentation
• Create and maintain sophisticated forecasting models for business planning, operations and capacity management
• Develop lead scoring, customer segmentation, propensity models, and predictive machine learning approaches to help nurture and grow customer accounts
• Establish engineering best practices, data modeling and scalable data infrastructure in collaboration with engineering teams
• Communicate complex analyses and recommendations effectively to executives and stakeholders across the organization
• Foster a collaborative, inclusive team culture that balances technical excellence with practical business impact

Preferred Qualifications

• Building data teams and practices in fast-paced, high-growth technology companies
• Financial modeling, capacity planning, chip optimization, or operations research
• Machine learning, NLP, or experience working with AI and large language models
• Go-to-market analytics, including marketing attribution, sales forecasting, and customer lifecycle analysis
• Knowledge of cloud based billing systems and standard accounting practices
• Managing through ambiguity and creating clarity in early-stage environments