Data Science Manager, GTM

The Manager of Data Science for GTM is a hands-on player coach who leads from the front across Sales, Customer Success, and Marketing analytics. They are deeply technical, opinionated about the GTM analytical agenda, and accountable for both the output of the team and the development of each person on it. They are not a pure people manager — they roll up their sleeves on the hardest problems, set the standard for what good looks like, and pull the team up through hands-on coaching, technical mentorship, and direct involvement in the work. They own the analytical strategy for the GTM function and are a credible, trusted partner to Sales, CS, and Marketing leadership.

• Set the analytical strategy and roadmap for GTM, proactively identifying the highest-leverage opportunities before they are asked, across the full customer lifecycle from acquisition through expansion and retention.
• Operate as a player coach, stay technical, get into the weeds on the hardest GTM problems, and model the analytical rigor and DS thinking you expect from the team.
• Coach the team directly on the work itself: review analyses, push on methodology, challenge correlation versus causation, and raise the bar for statistical depth, EDA, and storytelling.
• Own the measurement strategy for GTM, defining how we evaluate the true impact of marketing campaigns, sales motions, and CS interventions through rigorous causal methods, not just attribution heuristics.
• Drive development and iteration of churn risk, health scoring, and expansion propensity models, ensuring they are statistically sound and actively used by CS and Sales to prioritize accounts.
• Lead marketing attribution methodology, moving the team beyond last-touch toward multi-touch and incrementality-based approaches that give leadership an accurate picture of what is actually driving pipeline.
• Partner with Revenue Operations and Finance on pipeline and revenue forecasting models that inform quota setting, capacity planning, and board-level reporting.
• Partner with Sales, CS, and Marketing leadership as a strategic thought partner. Influence decisions on territory design, segmentation, campaign investment, and retention strategy before they are made.
• Grow each person on the team intentionally, set clear development goals, give direct feedback often, and hold the bar high on both performance and growth.
• Champion AI as a force multiplier. Build agents and skills that transition repetitive GTM data work into automated capabilities and free the team to focus on higher-leverage problems.

• 7+ years of experience in data science or quantitative research, with at least 2 years managing a team of data scientists.
• Strong hands-on technical skills: SQL, Python, and modern data warehouse environments. You can still write the code and review the work.
• Demonstrated experience applying analytical methods to GTM problems: churn modeling, propensity scoring, attribution, forecasting, or similar.
• Experience with causal inference methods (DiD, synthetic control, regression discontinuity, or similar) applied to real GTM or business problems.
• Deep experience with experimental design, A/B testing, and rigorous measurement frameworks in a marketing or sales context.
• Strong track record of influencing GTM strategy through data-driven recommendations at the leadership level.
• Exceptional storytelling skills, able to translate complex methodology and findings into clear, compelling narratives for Sales, CS, and Marketing leaders.
• Track record of developing data scientists and helping them grow from reactive analytics to proactive, strategic insight generation.

• Hands-on experience with churn risk modeling, health scoring, or customer lifetime value models in a B2B SaaS context.
• Experience with marketing attribution beyond last-touch, media mix modeling, incrementality testing, or multi-touch attribution.
• Familiarity with revenue forecasting and pipeline modeling in partnership with RevOps or Finance.
• Background in SaaS, developer tools, or B2B product-led growth metrics.
• Experience with CRM data (Salesforce or HubSpot) and marketing automation platforms as analytical data sources.
• Hands-on experience with AI tools like Claude, Cursor, or similar LLM-powered assistants and a track record of helping a team adopt them.
• Experience with probabilistic modeling or Bayesian approaches to forecasting and scoring.