The opportunity
Job Description: Data Scientist, B2B Demand Generation, Growth & Measurement
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 growth opportunities, and help marketers act on evidence at scale.
+ years in a quantitative role such as Data Science, B2B Demand Gen Marketing: Science, Growth Analytics, Decision Science, with meaningful experience supporting B2B marketing, demand generation, pipeline growth, or enterprise and SMB go-to-market motions.
What they're looking for
- Deep expertise in experimentation, causal inference, and applied statistics,: including incrementality testing and measurement strategies for multi-touch marketing journeys, limited observability, and long or heterogeneous sales cycles.
- Strong technical fluency in SQL and Python, with experience joining and: analyzing marketing-platform, web analytics, product, CRM, account, opportunity, and revenue data to produce reliable business insights.
- Proven ability to translate analysis into marketing decisions, including: channel and audience prioritization, campaign optimization, funnel improvements, budget allocation, forecasting, and go-to-market strategy.
- Strong business judgment and a bias toward action.
