Data Scientist, Marketing Innovation

Responsibilities

• 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 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.

Requirements

• 10+ years in a quantitative role such as Data Science, B2B Demand Generation Marketing Science, Growth Analytics, or Decision Science, with meaningful experience supporting B2B marketing, demand generation, pipeline growth, or enterprise and SMB go-to-market motions.
• 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.
• 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.
• Understanding of B2B funnel mechanics, account-based buying journeys, sales handoffs, lead and opportunity qualification, attribution tradeoffs, instrumentation, and the relationship between marketing investment, pipeline, and revenue.
• Excellent communication and cross-functional partnership skills.
• Hands-on experience using LLMs, agents, and modern AI tools to automate analysis, accelerate experimentation, and build scalable decision-support workflows.