Data Scientist, Marketing

The shape of it

In the posting’s own words

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.

What it asks for · 5

• 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

Also a plus

• 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

What the job covers

• 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

Tools and skills named

Words the posting leans on

• marketing19×
• measurement8×
• experience6×
• marketing mix5×
• metrics5×
• recommendations5×
• building4×
• causal4×
• mix modeling4×
• activation3×
• data science3×
• geo experiments3×
• incrementality3×
• lifecycle3×
• bringing marketing2×
• build2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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