As Heartflow's product portfolio expands, understanding how customers adopt, use, and get value from our products has become central to how we build and grow.

We are seeking a Product Analytics Manager to own product analytics for the Heartflow platform. Reporting to the Director of Strategic Analytics, you will be the analytical partner to Product, Engineering, and Commercial leaders, answering questions such as which features drive retention and utilization, what separates high-adoption sites from low-adoption ones, and whether a new capability actually changed behavior. You will design and run experiments, build the instrumentation and data foundations that make product behavior measurable, and turn insights into clear recommendations that shape the roadmap.

This is a hands-on, high-impact role for someone who is equally comfortable defining an event taxonomy with engineers, writing complex SQL, building an executive dashboard, and presenting a point of view to senior leadership. You will also help lead the way in applying AI to analytics, using modern AI tools to accelerate analysis, automate recurring work, and make product insights accessible across the company.

Job Responsibilities:

• Product insights & strategy: Own the analytical agenda for the Heartflow platform; identify the features, workflows, and behaviors that drive adoption, retention, and utilization, and translate findings into prioritized recommendations for Product and Commercial leadership.
• Experimentation: Design, run, and analyze experiments (A/B tests, feature flags, staged rollouts) with rigorous hypotheses, power analysis, and success metrics; where randomized tests are not feasible, apply quasi-experimental methods (e.g., difference-in-differences, matched cohorts, interrupted time series) to estimate impact.
• Metrics framework: Define and maintain a product metrics framework, including north-star, activation, engagement, retention, and feature adoption metrics, with clear, governed definitions that tie product usage to commercial outcomes such as site utilization and revenue.
• Instrumentation & tracking: Partner with Engineering to design and implement event tracking in Mixpanel and observability signals in Datadog; own the tracking plan and event taxonomy, and establish QA processes that keep product data accurate, complete, and trustworthy.
• Retention & cohort analysis: Build cohort, funnel, and lifecycle analyses at the user and site level to understand ramp, engagement, and churn risk; develop product health indicators that help Customer Success and Sales act early.
• Dashboards & self-service BI: Build and maintain dashboards and self-service reporting (e.g., Tableau, Mixpanel, semantic-layer tools) that give Product, Engineering, and executive stakeholders real-time visibility into product performance.
• Launch measurement: Define success criteria before launches, measure post-launch performance, and run structured readouts so the organization learns from every release.
• Voice of customer: Combine quantitative usage data with qualitative inputs (customer feedback, support tickets, surveys, user research) to build a complete picture of the customer experience.
• AI-enabled analytics: Champion the use of AI and LLM tools across the analytics workflow, from code generation and automated analysis to natural-language access to product data, and help build AI-assisted tools that scale the team’s impact.
• Data governance & compliance: Ensure product analytics practices follow Heartflow’s data handling, privacy, and quality policies, partnering with Data Engineering, Security, and Quality/Regulatory teams as needed in our regulated medical device environment.
• Communication & leadership: Present findings and recommendations to senior leadership with clear narratives and visuals; mentor analysts and raise the bar for analytical rigor across the team.

Skills Needed:

• Deep expertise in product analytics, including experimentation design and analysis, cohort and retention analysis, funnel analysis, and feature adoption measurement.
• Strong statistical foundation: hypothesis testing, power and sample-size analysis, regression, and causal inference techniques for situations where clean randomization is not possible.
• Advanced SQL and proficiency in Python for analysis, modeling, and automation; experience with cloud data warehouses (e.g., Amazon Redshift, Snowflake, BigQuery).
• Strong BI and data visualization skills with tools such as Tableau or similar; experience with semantic layers or metrics stores (e.g., Cube, dbt) is a plus.
• Hands-on experience with product analytics and observability platforms such as Mixpanel, Amplitude, or Datadog, including designing event schemas and tracking plans with Engineering.
• High AI fluency: daily, practical use of LLM tools (e.g., Claude, ChatGPT, Copilot) to accelerate analysis and coding, plus curiosity about how AI can automate analytics workflows and power new products.
• Ability to translate ambiguous business questions into structured analyses and to turn complex results into clear, actionable recommendations for technical and non-technical audiences.
• Proven cross-functional collaboration with Product, Engineering, Design, Customer Success, and Commercial teams; comfortable influencing without direct authority.
• Strong product sense and business acumen, with the ability to connect user behavior to customer value and company outcomes.
• Familiarity with healthcare data privacy requirements (e.g., HIPAA) and working in a regulated environment is a plus.
• Highly organized and self-directed, with a bias for action and a commitment to data quality and analytical rigor.

Educational Requirements & Work Experience:

• Bachelor’s degree in a quantitative field such as Statistics, Economics, Computer Science, Mathematics, Engineering, or a related discipline; Master’s degree preferred.
• 8+ years of experience in analytics, data science, or a related field, including at least 3 years focused on product analytics.
• Demonstrated track record of designing and analyzing experiments that influenced product decisions.
• Experience leading analytics instrumentation projects in partnership with Engineering.
• Experience in B2B SaaS, healthcare technology, or medical devices strongly preferred.
• Experience mentoring or managing analysts preferred.

The salary range for this position is $155,000 - $200,000 and cash bonus. #LI-Hybrid