Data Science Tech Lead Manager, Model Experience

Responsibilities

• Act as a strategic thought partner to product, engineering, and research leaders, helping shape model roadmap priorities, launch decisions, and the major bets the team makes
• Set the data science agenda for Model Experience, deciding where the team can have the most impact on model development and launch decisions and what it should deprioritize
• Hire, manage, and develop a team of data scientists, coaching them to move from answering questions to shaping decisions
• Frame the most important and ambiguous questions about model quality and user experience, size the opportunities, and make clear recommendations, including when they challenge the current direction
• Define how Model Experience measures success, establishing the core metrics and measurement frameworks across external products and internal testing that align teams and leadership on what matters
• Set the standard for experimentation and causal inference across Model Experience, including where launches lack a clean A/B test, and personally lead the highest-stakes analyses

• Turn complex analyses of model and user behavior into clear narratives and recommendations that drive decisions with senior leadership and cross-functional partners
• Establish foundational data practices and shape the analytics infrastructure Model Experience needs to support rapid iteration and decision-making as our models and products evolve

You may be a good fit if you

• Have extensive experience in data science, including leading analytics for a product or research area at significant scale
• Have 3+ years managing data science teams, including setting team direction, hiring, and developing senior data scientists
• Have been a trusted partner to senior product, research, and business leaders, with clear examples of changing a roadmap or high-stakes decision
• Bring strong product sense and business judgment, including the ability to frame the right question and take a position with incomplete information
• Have deep expertise in experimental design, causal inference, and statistical modeling at high scale, with enough fluency in Python and SQL to stay hands-on and guide others' technical work
• Communicate clearly in writing and in presentations, including to executive audiences
• Have a bias for action and thrive in ambiguous, fast-moving environments where you must create clarity and drive progress
• Care about the company's mission of building helpful, honest, and harmless AI
• Strong candidates may also have:
• Experience with AI/ML products, large language models, or developer tools in the AI/ML ecosystem
• Experience as a tech lead manager or player-coach, balancing hands-on technical work with people management
• Experience building or scaling a data science team in a fast-growing organization
• Experience working closely with Product or Research teams, especially on LLM or AI products
• Comfort working with unstructured data