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Data Science Product Manager Jobs in California (NOW HIRING)

Data Engineering Manager, Product

San Francisco, CA · On-site

$196K - $203K/yr

You will partner closely with Product Data Scientists, Product Managers, and Product Engineers to understand how users interact with Claude, how to measure product quality and growth, and how to ...

This is your chance to build a department, define a strategy, and implement a road-map to create and build data science products. The department will be part of a new innovation center, essentially a ...

Senior Product Manager

San Diego, CA · On-site

$140 - $190/hr

Our products help clinical scientists, medical writers, clinical operationsteams, and data managers make better decisions faster while maintaining transparency,trust, and regulatory compliance.Faro ...

Senior Product Manager

San Jose, CA

$148K - $195K/yr

Our products help clinical scientists, medical writers, clinical operations teams, and data managers make better decisions faster while maintaining transparency, trust, and regulatory compliance.

Senior Product Manager

San Diego, CA · On-site

$152 - $179/hr

Our products help clinical scientists, medical writers, clinical operations teams, and data managers make better decisions faster while maintaining transparency, trust, and regulatory compliance.

Senior Product Manager

San Jose, CA · On-site

$152 - $179/hr

Our products help clinical scientists, medical writers, clinical operations teams, and data managers make better decisions faster while maintaining transparency, trust, and regulatory compliance.

Senior Product Manager

San Diego, CA

$134K - $177K/yr

Our products help clinical scientists, medical writers, clinical operations teams, and data managers make better decisions faster while maintaining transparency, trust, and regulatory compliance.

Senior Product Manager

San Diego, CA · On-site +1

$134K - $177K/yr

Our products help clinical scientists, medical writers, clinical operations teams, and data managers make better decisions faster while maintaining transparency, trust, and regulatory compliance.

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Showing results 1-20

Data Science Product Manager information

See California salary details

$50.8K

$157.3K

$194.4K

How much do data science product manager jobs pay per year?

As of Sep 3, 2026, the average yearly pay for data science product manager in California is $157,317.00, according to ZipRecruiter salary data. Most workers in this role earn between $139,200.00 and $194,400.00 per year, depending on experience, location, and employer.

What is a Data Science Product Manager?

A Data Science Product Manager is a professional who bridges the gap between data science teams and business objectives by guiding the development of data-driven products. They work closely with data scientists, engineers, and stakeholders to define product vision, prioritize features, and ensure successful product delivery. Their role involves understanding both the technical aspects of machine learning and analytics as well as user needs and business strategy. This ensures that data-powered products are effective, user-focused, and aligned with organizational goals.

How does a Data Science Product Manager typically collaborate with data scientists and engineers during a product lifecycle?

A Data Science Product Manager plays a crucial role in bridging the gap between business objectives and technical teams. Throughout the product lifecycle, they work closely with data scientists to define project goals, prioritize features, and translate business needs into actionable data-driven solutions. They also coordinate with engineers to ensure the seamless integration of machine learning models into products, address technical constraints, and facilitate communication between cross-functional teams. This collaborative approach ensures that data science initiatives are both technically feasible and aligned with overall business strategy.

What are the key skills and qualifications needed to thrive as a Data Science Product Manager, and why are they important?

To thrive as a Data Science Product Manager, you need a strong background in product management, data analytics, and a foundational understanding of machine learning, often supported by a degree in a technical or quantitative field. Familiarity with tools like SQL, Python, JIRA, and knowledge of data platforms and agile methodologies is typically required. Excellent communication, strategic thinking, and the ability to bridge technical and non-technical teams are vital soft skills. These competencies ensure successful product development, effective stakeholder alignment, and the delivery of impactful data-driven solutions.

What is the difference between Data Science Product Manager vs Data Analyst?

AspectData Science Product ManagerData Analyst
Required credentialsBackground in data science, product management, or related fields; often requires experience with machine learning and data-driven product developmentTypically holds a degree in statistics, mathematics, or business; skills in data visualization and basic analytics
Work environmentCollaborates with product teams, data scientists, engineers; focuses on developing data products and strategiesWorks with business units to interpret data, generate reports, and support decision-making
Employer and industry usageUsed in tech companies, e-commerce, and organizations developing data-driven productsCommon across finance, marketing, healthcare, and business intelligence roles

The main difference is that Data Science Product Managers oversee the development of data products and strategies, requiring a blend of product management and data science skills. Data Analysts focus on interpreting data and generating insights to support business decisions. Both roles are essential in data-driven organizations but serve different functions within the data ecosystem.

What are popular job titles related to Data Science Product Manager jobs in California?

For Data Science Product Manager jobs in California, the most frequently searched job titles are:

What job categories do people searching Data Science Product Manager jobs in California look for?

The top searched job categories for Data Science Product Manager jobs in California are:

What cities in California are hiring for Data Science Product Manager jobs?

Cities in California with the most Data Science Product Manager job openings:

Infographic showing various Data Science Product Manager job openings in California as of August 2026, with employment types broken down into 85% Full Time, 14% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $157,317 per year, or $75.6 per hour.

Director, Data Science - Product Management

PlayStation Global

San Mateo, CA

$265K - $277K/yr

Full-time

Posted 9 days ago


Job description

Director, Data Science - Product Management San Mateo, CA

At Sony Interactive Entertainment, PlayStation creates unforgettable gaming experiences for millions of players around the world. As a global leader in interactive entertainment, we operate at massive scale across a complex digital ecosystem spanning hardware, software, services, commerce, marketing, and player engagement. 

About the Role 

We are seeking a Director of Data Science Product Management to lead the strategy, prioritization, and execution of data science to drive better decision-making and player experiences across PlayStation. 

This role is responsible for portfolio of products across the data science lifecycle - identifying high-value analytical opportunities, shaping predictive and decision products, and ensuring they deliver measurable business outcomes through adoption, monitoring and continuous improvement. The role will lead a team of Data Science Product Managers responsible for applying data science to solve business problems, including capabilities across customer lifetime value, player lifecycle intelligence, forecasting, simulations, audience intelligence and decision automation. 

The Director will help ensure that SIE's data science investments are focused on the highest-impact opportunities, built with strong product discipline, and adopted in the workflows where they can create measurable business value. 

Key Responsibilities 

  • Translate business priorities into structured problem statements, investment cases, product strategies, and measurable outcomes, ensuring data science work is managed as a scalable product portfolio rather than one-off analytical requests. 
  • Drive product strategy and lifecycle ownership for player value and decision intelligence capabilities, such as CLV, engagement and forecasting, ensuring high quality products are advanced from discovery through productionization, adoption, monitoring, iteration, and long-term value realization. 
  • Establish clear criteria for when analytical work should remain exploratory, become a recurring decision process, or graduate into a production-grade product. 
  • Define success metrics and quality standards for data science products, including business impact, adoption, reliability, interpretability, model quality, operational sustainability, and decision quality. 
  • Partner with Data Science and Analytics leaders to ensure rigor across experimentation, causal inference, forecasting, incrementality measurement, model evaluation, and value analysis, while promoting these approaches as core decision-making tools. 
  • Influence senior stakeholders across functions to align strategy, clarify priorities, manage tradeoffs, and drive adoption of data science products. 
  • Guide Product Managers across the product lifecycle while directly leading or sponsoring the organization's highest-impact and most ambiguous product initiatives. 
  • Create a strong Data Science product culture centered on hypothesis-driven discovery, evidence-based prioritization, measurable business impact and close partnership with cross-functional partners. 

Qualifications 

  • Bachelor's degree in Business, Data Science, Computer Science, Statistics, Economics, Engineering, or a related field. 
  • 15+ years of relevant experience, including 8+ years in digital product management, data product management, AI/ML product management, analytics product management, or related roles. 
  • 6+ years of people management experience, including experience leading Product Managers or senior product-oriented analytics leaders. 
  • Proven experience leading complex, cross-functional portfolios involving Data Science, ML Engineering, Analytics, Data Engineering, and business stakeholders. 
  • Strong understanding of data science and machine learning workflows, including experimentation, predictive modeling, forecasting, segmentation, recommender systems, lifecycle modeling, productionization, and monitoring. 
  • Experience with products or capabilities related to CLV/LTV, personalization, churn, propensity modeling, forecasting, audience targeting, marketing optimization, commerce, or digital engagement. 
  • Familiarity with modern data and ML ecosystems such as SQL, Python, Snowflake, Spark, feature stores, MLOps tools, model monitoring, APIs, and analytics platforms. 
  • Demonstrated ability to translate ambiguous business needs into product strategies, roadmaps, priorities, and measurable outcomes. 
  • Strong executive communication skills, including the ability to translate technical concepts into business impact. 
  • Experience leading, mentoring, and developing high-performing product teams. 

Preferred Skills 

  • Experience in gaming, digital commerce, subscription, marketplace, streaming, or consumer technology businesses. 
  • Deep experience with customer intelligence, player lifecycle analytics, personalization, experimentation, audience intelligence, or growth optimization. 
  • Experience defining success metrics and quality standards for data science products, including adoption, reliability, latency, interpretability, fairness, model drift, and business impact. 
  • Experience building product management capabilities, teams, or operating models from the ground up. 
  • Demonstrated ability to use AI-enabled tools, including generative or agentic AI, to improve product workflows, accelerate insight generation, and enhance decision-making.