• 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.

• Design and scale backend services that integrate Generative AI and Retrieval-Augmented Generation (RAG) for production use. • Develop AI agents to automate workflows and improve product intelligence. • Build pipelines that transform structured and unstructured data into actionable insights. • Optimize retrieval systems for speed, scalability, and user relevance. • Collaborate with product and engineering teams to deliver AI features that directly improve customer experience.

• Design, build, and optimize Machine Learning models powering origination, behavioral scoring, limit management, and early-warning systems • Leverage alternative data sources including transaction behavior, merchant data, and device intelligence to improve predictive performance • Evaluate and implement the infrastructure required to deploy, monitor, and retrain models at scale • Explore the applications of novel architectures (transformers, foundational models, etc.) for credit risk and related tasks • Collaborate with cross-functional teams to identify opportunities for data-driven insights and solutions • Work with other members of the Credit Risk team to build credit policies across the full customer lifecycle: origination, limit management, restructuring, and write-offs • A lot of autonomy and ownership: you will own at least one Machine Learning model end-to-end • Communicate findings and insights to technical and non-technical stakeholders

• Contribute to software systems used by millions of low-income Americans • Drive change through impactful data analysis and investigations • Maintain reliable data pipelines to ensure trustworthy data. • Work with product teams, starting with the marketplace team, but also potentially including the government benefits team, marketing, and growth. • Effectively communicate with team members during projects, code review, etc.