Growth Data Scientist (Generalist or Personalization) | Anthropic

The Growth team’s job is to keep Anthropic on the exponential by getting more people and companies to use and pay for Claude. Growth Data Scientists work closely with growth product managers, engineers, and marketers on experimentation, opportunity sizing, strategic analysis, metrics, and personalization. The team works in an AI-native way, using Claude as a collaborator for much of its analysis, reporting, and experiment work, and expects you to push that further. This posting covers two focus areas, and applicants should say in their application which fits best, or whether both appeal:

• Growth generalist: A strong generalist across marketing and product who drives experimentation, opportunity sizing, marketing measurement, and strategic analysis
• Personalization: Building and evaluating the models and experiments that choose the right message, offer, or product experience for each user across lifecycle marketing, onboarding, and in-product experiences

Key Responsibilities

Across both focus areas:

• Design and analyze A/B tests and quasi-experiments across activation, retention, and monetization, and turn the results into a clear call on what to ship
• Define systems of metrics and guardrails that tie short-term wins to long-term value
• Present analyses and recommendations to technical and non-technical audiences, leading with the decision and keeping the numbers in support

Growth generalist focus:

• Size opportunities and make the case for what Growth should work on next
• Measure the incrementality of paid and lifecycle marketing with holdouts, geo experiments, and other causal methods, and turn results into budget and channel recommendations
• Tie acquisition and lifecycle programs to long-term value (LTV, CAC, and payback)

Personalization focus:

• Build and ship targeting and personalization models that decide which users get which message, offer, or experience, working closely with growth product managers and engineers
• Design the tests that evaluate and improve the personalization strategy, including holdouts, bandits, and off-policy estimates where A/B tests aren’t practical

Requirements

• 7+ years of experience in data science, with significant time on growth, marketing, lifecycle, or personalization problems
• Deep expertise in experimental design and causal inference, including A/B testing at scale, holdouts, and quasi-experimental methods when randomization isn’t available
• Experience analyzing growth funnels: acquisition, activation, retention, and monetization
• Proficiency with Python and SQL
• Clear written communication, and a track record of recommendations that changed priorities or what a team built
• A bias for action and comfort in ambiguous, fast-moving environments
• Interest in Anthropic’s mission of building safe and beneficial AI
• Bachelor’s degree or an equivalent combination of education, training, and/or experience in a relevant field
• Years of experience required will correlate with the internal job level for the position

For the Growth generalist focus: Experience measuring marketing, including incrementality testing, holdouts or geo experiments, and LTV/CAC for paid or lifecycle programs

For the Personalization focus: Hands-on experience building and shipping machine learning models that drive user-facing decisions (targeting, ranking, recommendation, or personalization)

Skills

Technical Skills

• Python and SQL
• Experimental design and causal inference
• A/B testing, holdouts, and quasi-experimental methods
• Growth funnel analysis
• Machine learning for targeting and personalization (Personalization focus)
• Marketing incrementality and LTV/CAC measurement (Growth generalist focus)

Soft Skills

• Clear written and verbal communication
• Influencing priorities with data
• Bias for action
• Comfort with ambiguity

Nice to Have

• Experience defining activation metrics and lifecycle measurement for a product-led business
• Experience with experimentation platforms and meta-analysis across experiment portfolios
• Background at consumption-based, multi-product companies serving both consumers and enterprises
• Experience using LLMs or coding agents in your own analytical workflow
• Experience with marketing mix modeling, geo experiments, or synthetic controls
• Experience running contextual bandits, uplift models, or similar decision systems in production
• Experience with lifecycle or CRM personalization (email, push, in-product messaging) and its measurement problems, such as global holdouts and interference between campaigns

Benefits

• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Collaborative office space