Data Product Manager

Intuitive•Sunnyvale, CA

About The Position

The Product Manager will provide the Data Analytics & AI team an anchor and partner with cross-functional stakeholders at Intuitive to design, develop, and mature analytical capabilities. This person will deliver actionable business insights, drive data-informed product decisions, and architect robust analytical solutions enabling world-class product strategies. This critical role combines expertise in product analytics, business intelligence, data engineering, and data science to evaluate product health, user engagement, and adoption trends, and to identify and quantify opportunities for growth and improvement. The successful candidate is a detail-oriented, strategic thinker skilled in extracting value from data, designing scalable insight platforms, and communicating with diverse business and technology stakeholders.

Requirements

Bachelor's degree in engineering, mathematics, computer science, data science, statistics, finance, or a similar quantitative field
3–6 years of progressive experience in product analytics, business analytics, advanced analytics, or data science roles.
5+ years’ experience using data warehouse solutions and databases (using tools such as SQL, PL/SQL, etc.)
Strong SQL skills, ability to perform optimized querying involving multiple tables and subqueries.
Strong proficiency in Python (or R) for data analysis, modeling, and workflow automation.
Demonstrated experience designing and building dashboards using tools such as Tableau, Looker, Power BI, or similar BI platforms.
Solid understanding of statistical analysis, experimental design, data modeling, and practical application of analytics methods in business settings.
Experience working with business system requirements and building solutions.
Experience gathering/documenting business requirements, using industry standard business intelligence tools to extract data, formulate metrics and build reports
Experience with cloud platforms and the modern data stack (AWS, Azure, GCP, Snowflake, Redshift, BigQuery, Airflow).
Familiarity with analytics modeling tools.
Prior experience in data architecture, data pipeline design, and implementation of data governance/quality controls.
Working knowledge of cloud-based data environments and efficient data engineering workflows.
Familiarity with machine learning, predictive modeling, and relevant frameworks (scikit-learn, TensorFlow, etc.)

Nice To Haves

Product analytics and KPI development
Advanced data analysis and visualization
Experimental design (A/B and multivariate testing)
Data storytelling and business insight generation
Business problem solving and product strategy support
Insights architecture and data product development
Stakeholder engagement and cross-functional collaboration
Excellent written and verbal communication skills
Intellectual curiosity and continuous process/data improvement mindset
Strong problem-solving, quantitative, and analytical abilities; Solution-oriented
Ability to effectively prioritize projects, manage multiple competing priorities simultaneously and drive projects to completion

Responsibilities

Define, measure, and report key product KPIs and success metrics, ensuring alignment across the commercial org.
Analyze user engagement, product adoption, and feature utilization through cohort analysis and exploratory data analysis.
Identify product trends, pain points, and growth opportunities.
Apply statistical techniques, machine learning models, and predictive analytics to understand and optimize product performance.
Conduct behavioral analysis, segmentation, and deliver recommendations to support increased product adoption and value realization.
Lead the design, execution, and analysis of A/B tests and experimentation to support hypothesis-driven product enhancements.
Develop and iterate on models to predict user engagement, churn, and feature impact.
Lead the design, development, and deployment of AI/ML models, including large language model (LLM) integrations and autonomous agentic pipeline
Support data-driven prioritization, rapid experimentation, and iterative product development.
Collaborate with data engineering teams to define, maintain, and improve clean and governed datasets.
Ensure the consistency, accuracy, and accessibility of key business and product metrics across dashboards and reporting tools.
Contribute to the design and build of enterprise data products, self-serve insight platforms, and analytical tools that empower partners across the organization.
Act as the analytical partner to commercial stakeholders in product, engineering, and business operations.
Present findings in clear, concise formats tailored for both technical and non-technical audiences.
Contribute to strategic planning by providing data-informed perspectives and recommendations.