Tech Lead Manager, Growth Data Science

About the Role

As part of our growing Data Science and Analytics team, you will play an instrumental role in our company’s mission of building safe and beneficial artificial intelligence by driving data-informed decision making across our organization. You bring experience from high-bar teams and are eager to help shape the norms and best practices of a growing data science team as Anthropic continues to scale. In this unique company, technology, and moment in history, your work will be critical to informing our strategy as we deploy safe, frontier AI at scale to the world.

You’ll be the senior data science leader embedded in our Growth team and a strategic thought partner to its product and marketing leads. The Growth team’s job is to keep Anthropic on the exponential by getting more people and companies using and paying for Claude. You’ll help set direction across acquisition, activation, retention, and monetization: bringing a point of view on where to invest, how to grow revenue, and how to deliver more value for our users. As a tech lead manager, you’ll also manage a small team of Product Data Scientists, setting their agenda and raising the bar for how data shapes growth decisions.

Key responsibilities

• Act as a strategic thought partner to Growth product and marketing leads, helping shape growth strategy, roadmap priorities, and the major bets the team makes

• Set the data science agenda for Growth, deciding where the Product Data Science team can have the most impact and what it should deprioritize

• Manage and develop a small team of Product Data Scientists

• Frame the most important and ambiguous questions facing Growth, from where the next users and companies will come from to what makes them stay and pay, size the opportunities, and make clear recommendations, including when they challenge the current direction

• Define how Growth measures success, establishing the core metrics, guardrails, and measurement frameworks that tie short-term wins to long-term value

• Set the standard for experimentation and causal inference across Growth, including holdouts and quasi-experimental methods where A/B tests aren’t practical, and personally lead the highest-stakes analyses

• Guide how Growth measures the return on its programs, including marketing incrementality, LTV, CAC, and payback, and turn the results into budget and channel recommendations

• Guide the team’s work on targeting and personalization, including how we decide which users get which message, offer, or experience and how we evaluate those decisions

• Turn complex analyses into clear narratives and recommendations that drive decisions with senior leadership and cross-functional partners

• Establish foundational data practices and shape the analytics infrastructure Growth needs to support rapid iteration and decision-making as our products grow

Minimum qualifications

• Have extensive experience in data science, including leading analytics for a growth or product area at significant scale

• Have 5+ years managing data science teams, including setting team direction and developing senior data scientists

• Have been a trusted partner to senior product, growth, or marketing leaders, with clear examples of changing a roadmap or high-stakes decision

• Bring strong product sense and business judgment across the growth funnel, 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

Preferred qualifications

• Experience measuring marketing incrementality, including holdouts, geo experiments, or marketing mix modeling

• Experience with targeting, personalization, or uplift models in production

• Background at consumption-based, multi-product companies serving both consumers and enterprises

• Experience with AI/ML products or large language models, including using coding agents in your own analytical workflow