OpenAI

Data Scientist, B2B Demand Generation, Growth & Measurement

San Francisco, California, United StatesFull time$293,000 - $385,000 / yearPosted 6 days ago
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Job Description: Data Scientist, B2B Demand Generation, Growth & Measurement

About the Role

We are hiring a Data Scientist to lead measurement, experimentation, and decision science for B2B marketing demand generation. You will help us understand which marketing investments create incremental demand, qualified pipeline, and revenue and how to scale them efficiently.

Our mandate is to build a rigorous, full-funnel view of how B2B marketing creates demand and moves prospects from awareness and engagement to qualified opportunities, closed-won revenue, and expansion. You will shape how we measure marketing impact and influence across channels, campaigns, audiences, and account segments.

In this role, you will partner closely with B2B Marketing, Demand Generation, Growth, Sales, RevOps, Finance to connect marketing activity to qualified pipeline, customer acquisition, and efficient revenue growth.

What You’ll Do

- Define north-star, leading, and guardrail metrics for B2B demand generation, including account engagement, qualified leads and opportunities, sourced and influenced pipeline, conversion rates, pipeline velocity, and incremental ARR.

- Design and execute measurement and experimentation strategies across channels and campaigns, using randomized tests, audience or geographic holdouts, lift studies, quasi-experimental methods, and other causal approaches suited to long B2B sales cycles.

- Analyze channel, audience, campaign, creative, content, landing-page, and account-segment performance to identify the drivers of qualified demand, funnel conversion, pipeline quality, and incremental revenue.

- Partner with Marketing, Sales, RevOps, Finance, Product, and Engineering to improve instrumentation, campaign taxonomy, CRM data quality, lead-to-account matching, and the operating cadence for acting on measurement insights.

- Build AI-native measurement and decision-support workflows, using LLMs and agents to synthesize campaign performance, surface ...