Data Scientist, Marketing

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As part of our growing Data Science and Analytics team, you will own the measurement strategy behind Anthropic's marketing investment. This is a foundational role, building marketing measurement at Anthropic from the ground up. Your first focus is paid media. We are bringing marketing mix modeling in-house, and you will build and operate the econometrics toolkit — marketing mix modeling (MMM), geo experiments, synthetic controls, and incrementality testing — that tells us which marketing investments actually drive growth, working in close partnership with our paid marketing data scientist. From there, you'll extend the same causal rigor to lifecycle and other marketing programs: defining success metrics oriented on activation and sustained usage, and building self-serve measurement that scales beyond any one embedded analyst. Key responsibilities · Own incrementality measurement for paid media: build and operate our in-house marketing mix model, and design the geo experiments, synthetic-control studies, and holdouts that validate and calibrate it · Translate measurement results into budget and channel recommendations that shape how marketing invests · Establish primary success metrics and guardrails for lifecycle marketing, anchored on activation and active usage rather than reach · Develop hypotheses on marketing interventions, design experiments or causal inference studies, analyze results, and make recommendations based on impact to key metrics · Make marketing measurement self-serve by establishing the metrics, tooling, and best practices that let marketing partners answer routine questions without a data scientist in the loop · Present complex technical analyses and recommendations to both technical and non-technical audiences Minimum qualifications · Hands-on experience with marketing incrementality methods, including marketing mix modeling, geo experiments, synthetic controls, and A/B or holdout testing at scale · Proficiency with causal inference and machine learning methods, and judgment about when each is appropriate · Proficiency with Python and SQL · Experience applying data science within a Marketing or Growth context · Ability to communicate complex analyses as clear recommendations for non-technical audiences Preferred qualifications · 7+ years of experience in data science, with significant time embedded in Marketing or Growth teams · Experience building measurement frameworks from the ground up, moving teams from descriptive reporting toward causal understanding · A track record of translating complex analyses into recommendations that senior marketing stakeholders act on · Experience bringing marketing mix modeling in-house, or operating one end-to-end rather than through a vendor · Experience defining activation metrics and lifecycle measurement for a product-led business · Background at consumption-based, multi-product companies serving both consumers and enterprises · Comfort setting direction and making decisions when requirements are still taking shape · An interest in Anthropic's mission of building safe and beneficial AI For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $285,000—$380,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.