Posted today · be early
Staff Data Scientist - Growth & Expansion
HighLevel
IndiaremotePosted 1 day ago
Skill Required
Data-ScientistGrowth-AnalystProduct-AnalystBusiness-AnalystStaff-Data-ScientistGrowth-Data-ScientistUser-Growth-Data-ScientistGrowth-Data-ScienceData-ScienceData ScientistMicroservicesEngineeringStatisticsanalyticalautomationSnowflakesimilar)analyticsbuildingPythondesigndbtSQLandGoAIFulltime
Key highlights
- Experience: 9+ years in product/growth analytics, data science, or applied statistics
- Requirement: Strong SQL, Python, and Snowflake + dbt environment comfort
- Benefit: Global, remote-first organization
Role overview
HighLevel is an AI-powered business operating system providing infrastructure for agencies, entrepreneurs, and SMBs to build, automate, and scale. The company operates as a global, remote-first organization supporting over 1 million businesses across 150+ countries. HighLevel is hiring a Staff Data Scientist, Growth & Expansion to own the customer growth outcome across the organization. This cross-functional role connects onboarding, activation, trial-to-paid, multi-product adoption, and expansion efforts across Growth, GTM, Finance, and product teams, utilizing a B2B2C model to ensure the company invests in compounding value.
Responsibilities
- 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
Requirements
- 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
Nice to have
- 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
Additional details
- Reporting Structure: Report centrally to Product Analytics & Data Science for craft and standards while carrying the growth outcome across organizational boundaries.
- Success Metrics: Growth, GTM, Finance, and product teams share one trusted value model connecting adoption to TTP, retention, and expansion; Expansion drivers are named, quantified by segment, and on the roadmap; Leaders make roadmap and spend calls off analysis; The customer-growth mandate has reusable patterns and the foundation to scale beyond one IC.
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- Originally posted on Himalayas
