Product Analytics Manager

Description

The company is a publicly traded medical technology business (HTFL) working on diagnosis and management of coronary artery disease, the leading cause of death worldwide. Its flagship product is an AI-driven, non-invasive cardiac test, supported by ACC/AHA chest pain guidelines, that produces a color-coded 3D model of a patient's coronary arteries showing how blockages affect blood flow to the heart.

The platform covers the CCTA pathway with three tools: one to find coronary artery stenoses, one to assess coronary blood flow, and one to characterize and quantify atherosclerosis. The product line and the team are both growing.

The product is cleared in the US, UK, Europe, Japan and Canada, is backed by medical societies internationally, and has been used on more than 750,000 patients.

As the product portfolio grows, knowing how customers adopt, use and get value from the products now drives how the company builds and grows them.

The role is Product Analytics Manager, owning product analytics for the platform and reporting to the Director of Strategic Analytics. You will be the analytical partner to Product, Engineering and Commercial leaders, answering questions like which features drive retention and utilization, what sets high-adoption sites apart from low-adoption ones, and whether a new capability changed behavior. You will run experiments, build the instrumentation and data foundations that make product behavior measurable, and turn findings into recommendations that shape the roadmap.

This is a hands-on role. You will define event taxonomies with engineers, write complex SQL, build executive dashboards and present your views to senior leaders. You will also help lead the use of AI in analytics, using modern tools to speed up analysis, automate recurring work and make product insights easy for everyone to reach.

Responsibilities

• Own the analytics agenda for the platform: find the features, workflows and behaviors that drive adoption, retention and utilization, and turn them into ranked recommendations for Product and Commercial leadership.
• Design, run and analyze experiments such as A/B tests, feature flags and staged rollouts, with clear hypotheses, power analysis and success metrics. Where randomization is not possible, use quasi-experimental methods like difference-in-differences, matched cohorts and interrupted time series.
• Define and maintain a governed product metrics framework covering north-star, activation, engagement, retention and feature adoption, linking product usage to site utilization and revenue.
• Work with Engineering to design and build event tracking in Mixpanel and observability signals in Datadog. Own the tracking plan and event taxonomy, and set up QA so product data stays accurate, complete and trustworthy.
• Run cohort, funnel and lifecycle analyses at user and site level to understand ramp, engagement and churn risk. Build product health indicators so Customer Success and Sales can act early.
• Build and maintain dashboards and self-service reporting in tools such as Tableau, Mixpanel and semantic-layer platforms, giving Product, Engineering and executives near real-time visibility into performance.
• Set success criteria before each launch, measure results afterward, and lead structured readouts so the organization learns from every release.
• Combine usage data with qualitative input from customer feedback, support tickets, surveys and user research to get a full view of the customer experience.
• Lead the use of AI and LLM tools across the analytics workflow, from code generation and automated analysis to natural-language access to product data. Help build AI-assisted tools that扩大 the team's reach.
• Make sure product analytics follows the company's data handling, privacy and quality policies, working with Data Engineering, Security and Quality/Regulatory teams in a regulated medical device setting.
• Present findings and recommendations to senior leaders with clear stories and visuals. Mentor analysts and raise the standard of analytical rigor across the team.

Requirements

• Deep experience in product analytics, including experiment design and analysis, cohort and retention analysis, funnel analysis and feature adoption measurement.
• Strong statistics background: hypothesis testing, power and sample-size analysis, regression and causal inference for cases without clean randomization.
• Advanced SQL and Python for analysis, modeling and automation, with experience in cloud data warehouses such as Amazon Redshift, Snowflake or BigQuery.
• Strong BI and visualization skills in Tableau or similar tools. Experience with semantic layers or metrics stores such as Cube or dbt is a plus.
• Hands-on work with product analytics or observability platforms such as Mixpanel, Amplitude or Datadog, including designing event schemas and tracking plans with Engineering.
• High AI fluency: you use LLM tools such as Claude, ChatGPT or Copilot daily to speed up analysis and coding, and you are curious about automating analytics and powering new products with AI.
• Ability to turn ambiguous business questions into structured analyses and complex results into clear recommendations for both technical and non-technical audiences.
• Proven record of working across Product, Engineering, Design, Customer Success and Commercial teams, and influencing without direct authority.
• Strong product sense and business judgment, with the ability to link user behavior to customer value and company outcomes.
• Familiarity with healthcare privacy rules such as HIPAA and with regulated environments is a plus.
• Organized and self-directed, with a bias for action and a commitment to data quality and analytical rigor.
• Bachelor's degree in a quantitative field such as Statistics, Economics, Computer Science, Mathematics, Engineering or a related discipline. A Master's degree is preferred.
• 8+ years in analytics, data science or a related field, including at least 3 years in product analytics.
• A documented record of designing and analyzing experiments that shaped product decisions.
• Experience leading analytics instrumentation projects with Engineering.
• Experience in B2B SaaS, healthcare technology or medical devices is strongly preferred.
• Experience mentoring or managing analysts is preferred.

Conditions

Salary range is $155,000 - $200,000, plus a cash bonus.

Hybrid work model.

The company is an equal opportunity employer and does not discriminate on any protected basis, across all employment decisions.

Benefits

• Cash bonus in addition to base salary.