Data Product Manager, Analytics & AI

Intuitive

Sunnyvale (CA)

On-site

USD 141,000 - 239,000

Full time

5 days ago

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Job summary

Intuitive is seeking a Product Analytics Manager to anchor the Data Analytics & AI team and partner with cross-functional stakeholders to design, develop, and mature analytical capabilities. You will deliver actionable insights, drive data-informed product decisions, and architect robust analytics solutions enabling world-class product strategies.

The role combines expertise in product analytics, BI, data engineering, and data science to evaluate product health, user engagement, and adoption

Qualifications

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 with strong SQL skills
Proficiency in Python (or R) for data analysis, modeling, and automation
Experience designing dashboards using Tableau/Looker/Power BI
Understanding of statistics, experimental design, data modeling, and business analytics
Experience gathering business requirements and building BI solutions
Experience with cloud platforms and the modern data stack (AWS, Azure, GCP, Snowflake, Redshift, BigQuery, Airflow)
Familiarity with ML, predictive modeling and related frameworks
Experience in data architecture, data pipelines, and data governance/quality controls
Knowledge of cloud-based data environments and efficient data engineering workflows
Familiarity with ML frameworks (scikit-learn, TensorFlow)

Responsibilities

Define, measure, and report product KPIs and success metrics
Analyze user engagement, product adoption, and feature utilization
Identify product trends, pain points, and growth opportunities
Apply statistical methods, ML models, and predictive analytics to optimize performance
Conduct behavioral analysis, segmentation, and provide adoption recommendations
Lead A/B testing and experimentation to support product improvements
Develop models to predict engagement, churn, and feature impact
Design, develop, and deploy AI/ML models including LLM integrations
Collaborate with data engineering to define governed datasets
Ensure accuracy and accessibility of metrics across dashboards
Contribute to enterprise data products and self-serve insight platforms
Act as analytical partner to stakeholders and present findings clearly
Support strategic planning with data-informed perspectives