Senior Director – Data Science & Analytics
HighLevel
full-time
Posted on:
Location Type: Remote
Location: Texas • United States
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Job Level
Tech Stack
About the role
- 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.
- Leverage the modern data stack (Snowflake, dbt, Atlan, Hex, etc.) to enable advanced analytics, causal inference, and machine learning at scale.
- 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.
- Establish self-serve experimentation tools and centralized KPI definitions to accelerate data-driven product development.
- Partner with product leadership to translate analytics insights into roadmap prioritization, UX improvements, and feature impact assessments.
- Design, train, and productionize predictive and prescriptive models that optimize retention, churn, pricing, lead scoring, and campaign automation.
- Collaborate with platform teams to build and maintain feature stores, model registries, and evaluation pipelines for reproducibility and compliance.
- Integrate machine learning and generative AI into the HighLevel platform to enhance personalization, automation, and user productivity.
- Define and monitor model performance metrics (e.g., precision, recall, uplift, business ROI) and ensure continuous retraining and quality control.
- Partner with GTM, Finance, and Operations to quantify the impact of models, experiments, and analytics on revenue, efficiency, and customer lifetime value.
- Deliver predictive dashboards, simulations, and causal analyses that complement BI reporting and drive strategic decisions.
- Build forecasting and optimization systems that connect directly to core business metrics like MRR, churn, LTV/CAC, and NPS.
- Provide the analytical backbone for IPO-readiness through measurable, model-driven insights and defensible forecasting.
- Define success metrics for all data science and analytics initiatives and track performance against strategic goals.
- Collaborate with the data platform organization to ensure model governance, lineage, and data quality are enforced within existing pipelines.
- Evangelize statistical literacy, experimental rigor, and causal thinking across all functions to raise decision-making maturity company-wide.
- Foster a culture of curiosity, reproducibility, and accountability in every analytics and modeling effort.
Requirements
- 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.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work hours
- Professional development opportunities
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
data scienceproduct analyticsmachine learningPythonSQLRTensorFlowPyTorchA/B testingcausal inference
Soft Skills
strategic communicationleadershipcollaborationcuriosityaccountabilitystatistical literacyexperimental rigordecision-making maturitycross-functional collaborationtranslating complex data