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 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.

Requirements

• 10+ years in a quantitative role such as Data Science, B2B Demand Gen Marketing Science, Growth Analytics, Decision Science, with meaningful experience supporting B22B 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 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.
• Strong 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. You can influence marketers, sales and finance leaders, data scientists, product managers, and engineers with clear recommendations, credible evidence, and transparent tradeoffs.
• AI-native mindset and hands-on experience using LLMs, agents, and modern AI tools to automate analysis, accelerate experimentation, and build scalable decision-support workflows.