Staff Data Scientist - Growth & Expansion

We're hiring a Staff Data Scientist, Growth & Expansion to own one outcome - customer growth - across every team that shapes it. Growth at HighLevel isn't the property of a single team: onboarding, activation, trial-to-paid, multi-product adoption, and expansion are driven by the Growth org, GTM, Finance, and product teams across the company. Your job is to connect those efforts with a rigorous, segment-level understanding of what actually turns a new account into a growing, multi-product customer - in a B2B2C model where an agency signs up and then activates sub-accounts - so the company invests behind what compounds, not just what converts.

This is a broad, cross-functional role. You'll work closely with Growth, GTM, Finance, and any product team whose work drives customer growth; you report centrally to Product Analytics & Data Science for craft and standards and carry the growth outcome across organizational boundaries. You'll work amid a data foundation still being built, consuming governed and certified sources rather than rebuilding source logic, and raising the bar as you go. This is a hands-on, direction-setting Staff role - you advise Growth, GTM, and Finance leaders and set growth-measurement standards that analysts on adjacent teams adopt, with a path to grow a pod as the mandate scales.

• Own the customer-growth outcome end to end - onboarding, activation, TTP, multi-product adoption, and expansion revenue - across Growth, GTM, and the product teams that drive it, with clear, trusted metrics at each stage
• Identify which early and mid-lifecycle behaviors predict expansion (second product, higher plan, add-on attach), not just initial conversion, and turn it into a prioritized growth agenda
• Connect product signal to GTM/marketing spend and Finance's growth targets - a single evidence base the whole growth motion shares
• Model the B2B2C dynamic - agency → sub-account activation and expansion - and surface where compounding value is created or lost
• Partner with the Experimentation & Causal Inference lead to design and read growth experiments rigorously; hold causal vs. correlational claims to a real bar
• Partner with the Product Strategy & Growth org on the TTP/churn and add-ons charters so definitions and models are shared, not duplicated
• Set the technical direction for how customer growth is measured company-wide - own the canonical metrics, segment definitions, and value model on governed, certified data that other teams build on
• Build the growth-measurement and causal-inference framework - the standards and reusable methods that Analytics Engineering and adjacent DS teams reuse beyond this mandate
• Translate findings into decision-grade guidance for Growth, GTM, Finance, and product leaders; influence roadmap and investment without owning them
• Act as a trusted analytical advisor to Growth, GTM, and Finance leaders, and set the analytical standards that DS and analysts on adjacent teams adopt - raising the bar without direct authority
• Flag data gaps to Analytics Engineering and shape the event taxonomy the funnel and value model depend on
• Use AI tooling (Claude and similar) to move faster on exploration, documentation, and analysis

• 9+ years in product/growth analytics, data science, or applied statistics, with deep experience across activation, retention, conversion, and expansion motions
• Track record where you built a metric or value framework that multiple teams drove real outcomes with - this role owns an outcome across boundaries, not reports for one team
• Strong applied statistics - you design analysis to the causal question and know the failure modes of correlational reads
• Fluency partnering on experiments (A/B design, power, guardrails) and interpreting results honestly
• Strong SQL and working proficiency in Python; comfort in a Snowflake + dbt environment
• Experience turning behavioral and revenue data into segment-level insight that changed a product, growth, or GTM decision
• Comfort amid imperfect, in-progress data - you consume governed sources and raise the bar rather than rebuilding pipelines
• Cross-functional influence - you align Growth, GTM, Finance, and product leaders on shared numbers without direct authority

• B2B SaaS, CRM, or product-led growth background, especially freemium/trial and land-and-expand motions
• Usage-based/consumption or add-on revenue exposure (expansion surfaces)
• Familiarity with Statsig or a comparable experimentation platform
• Exposure to AI-assisted analytics workflows; experience mentoring analysts

• Growth, GTM, Finance, and product teams share one trusted value model connecting adoption to TTP, retention, and expansion - and invest behind it
• Expansion drivers (not just conversion drivers) are named, quantified by segment, and on the roadmap across the teams that own them
• Leaders across the growth motion make roadmap and spend calls off your analysis, not gut feel
• The customer-growth mandate has reusable patterns and the foundation to scale beyond one IC