Senior Director - Data Science & Analytics

HighLevel•Texas, TX

5h•Remote

About The Position

HighLevel is an AI powered, all-in-one white-label sales & marketing platform that empowers agencies, entrepreneurs, and businesses to elevate their digital presence and drive growth. We are proud to support a global and growing community of over 2 million businesses, comprised of agencies, consultants, and businesses of all sizes and industries. HighLevel empowers users with all the tools needed to capture, nurture, and close new leads into repeat customers. As of mid 2025, HighLevel processes over 15 billion API hits and handles more than 2.5 billion message events every day. Our platform manages over 470 terabytes of data distributed across five databases, operates with a network of over 250 microservices, and supports over 1 million domain names. Our People With over 1,500 team members across 15+ countries, we operate in a global, remote-first environment. We are building more than software; we are building a global community rooted in creativity, collaboration, and impact. We take pride in cultivating a culture where innovation thrives, ideas are celebrated, and people come first, no matter where they call home. Our Impact As of mid 2025, our platform powers over 1.5 billion messages, helps generate over 200 million leads, and facilitates over 20 million conversations for the more than 2 million businesses we serve each month. Behind those numbers are real people growing their companies, connecting with customers, and making their mark - and we get to help make that happen. Who You Are As Sr. Director, Data Science at HighLevel, you turn data into foresight. You lead the modeling, experimentation, and analytics disciplines that transform how the company understands customers, measures product impact, and drives growth.You see patterns in behavior, not just in data. You connect signals across millions of users to predict outcomes, optimize experiences, and guide decisions. You are equally comfortable in code and strategy, designing experiments one day and framing their business impact the next.You build high-performing teams that blend data science, applied ML, and product analytics, and you partner closely with Product, Growth, and Engineering to embed intelligence directly into the platform. You understand that data science is not a back-office function; it is how HighLevel learns, adapts, and scales.

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

Nice To Haves

Experience supporting IPO readiness or large-scale data governance a major plus

Responsibilities

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.