Own HighLevel’s end-to-end data science and product analytics strategy, focused on modeling, experimentation, and insight generation, built on the company’s governed data platform.
Build and lead a global team spanning data science, applied ML, decision science, and product analytics, partnering closely with data engineering and platform teams to ensure scalability and reliability.
Collaborate cross-functionally with Product, Growth, Marketing, and Engineering to ensure experiments, models, and insights directly inform product development, GTM decisions, and customer outcomes.
Oversee product analytics, defining how user behavior, engagement, and retention are measured, instrumented, and interpreted.
Build and scale experimentation and A/B testing frameworks, ensuring statistical rigor and consistent methodology across 50+ product and marketing teams.
Design, train, and productionize predictive and prescriptive models that optimize retention, churn, pricing, lead scoring, and campaign automation.
Partner with GTM, Finance, and Operations to quantify the impact of models, experiments, and analytics on revenue, efficiency, and customer lifetime value.

12+ years in data science, analytics, or ML roles, including 5+ years in senior leadership within SaaS or B2B2C companies
Proven track record establishing and growing data science and product analytics teams that translate governed data into actionable models, experiments, and insights driving business growth.
Expertise in Python, SQL, R, machine learning frameworks (TensorFlow, PyTorch), with strong applied experience in experimentation, causal inference, and model evaluation
Proven experience leading product analytics, defining instrumentation, event taxonomies, and metric frameworks that tie directly to user behavior and product outcomes
Deep understanding of A/B testing, causal inference, and experimental design at scale (50+ teams, automated frameworks)
Experience operationalizing models with shared feature stores, model registries, and automated retraining pipelines in partnership with data engineering
Experience developing AI-driven product features and operationalizing ML models at scale
Strong understanding of experimentation, attribution modeling, and business intelligence systems
Strategic communicator with the ability to translate complex data into compelling business narratives
Experience supporting IPO readiness or large-scale data governance a major plus.

VP, Data

Key skills