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Data Science Product Manager Jobs in California (NOW HIRING)

PROPRIUS

Director Data Science

San Francisco, CA

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 ...

Hinge Health, Inc

Data Science Manager

San Francisco, CA · On-site

$251K - $376K/yr

We are seeking a Manager, Data Science to lead the data strategy for two critical areas of our ... Product Data Quality & Infrastructure: Drive the "Product Data Quality" initiative for your domains.

Whatnot

Manager, Data Science

San Francisco, CA · On-site

$221K - $320K/yr

Partner closely with Product, Engineering, Marketing, Finance, Trust & Safety, and other cross ... Manage, coach, and develop high-performing data scientists while hiring thoughtfully as the ...

Adobe

Senior Manager, GTM Data Science

San Francisco, CA

$149K - $196K/yr

Adobe's creative products empower millions of customers-from individual creators and creative ... of data science, product analytics, marketing analytics, and business performance management.

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All JobsData Science Product JobsData Science Product Manager JobsData Science Product Manager Jobs in California

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 Jul 31, 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.

What is the hottest job of the 21st century?

Data Science Product Managers are among the most in-demand roles in the 21st century, combining skills in data analysis, product development, and strategic planning. They oversee data-driven products and require knowledge of tools like SQL, Python, and machine learning, often working in fast-paced tech environments. The role is expected to grow as organizations increasingly rely on data to make decisions.

Is 40 too late for data science?

A Data Science Product Manager can enter the field at age 40, as experience, domain knowledge, and skills like programming and statistical analysis are highly valued. Many professionals transition into data science roles later in their careers, and continuous learning through certifications or courses can facilitate this shift. Age is less important than relevant skills and experience in the data science industry.

Can data scientists make $300k?

Data science product managers and senior data scientists with extensive experience, specialized skills, and working in high-cost-of-living areas can earn salaries of $300,000 or more. Achieving this level often requires advanced knowledge of machine learning, strong business acumen, and leadership responsibilities, along with experience at top companies or in executive roles.

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.

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.

Can a data scientist be a product manager?

A data scientist can transition to a product manager role, especially if they develop skills in project management, user experience, and business strategy. While the roles have different focuses—data scientists analyze data and product managers oversee product development—both require strong communication and cross-functional collaboration. Experience with tools like A/B testing, roadmapping, and stakeholder management can facilitate this transition.
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:
Data Science Product Manager jobs near you
Infographic showing various Data Science Product Manager job openings in California as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $157,317 per year, or $75.6 per hour.

Data Engineering Manager, Product

Anthropic

San Francisco, CA

$196K - $203K/yr

Other

Re-posted 22 days ago

Job description

About the role

As a Data Engineering Manager focused on Product, you will build and lead the analytics engineering team responsible for creating the data foundations that enable data-driven decision making across Anthropic's Product organization.  You will oversee the development of scalable data solutions for Product pillars - including Consumer, Claude Code, Enterprise & Verticals, Growth, Platform Product - managing a team of analytics engineers and working closely with stakeholders across Data Science, Product, and Engineering to ensure teams have access to reliable, accurate metrics that can scale with our company's growth.

In this role, you will balance hands-on technical leadership with people management, setting the strategic vision for product data foundations while developing and mentoring team members.  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 transform raw event logs into insightful data marts that power product decisions.

Responsibilities:

  • Build and scale the Product Analytics Engineering team, including hiring and mentoring a team of high-performing analytics engineers embedded with Product pillars

  • Define and execute the strategic roadmap for product data foundations and analytics capabilities

  • Oversee the design and implementation of scalable data pipelines, data models, and analytics solutions that transform raw product event logs into canonical datasets and insightful data marts

  • Partner with Data Science, Product, and Engineering leadership to understand data needs and translate them into technical requirements

  • Establish and maintain high data integrity standards, SLAs, alerting, and best practices for the team

  • Drive the development of foundational data products, dashboards, and tools to enable self-serve analytics; partner with the Data Science team to build innovative data tools using Claude to scale data-driven decisions across Product teams

  • Foster a culture of technical excellence, continuous learning, and data-driven decision making

  • Serve as a technical thought leader for data modeling, ETL processes, and product analytics infrastructure

You might be a good fit if you have:

  • 8+ years of experience managing analytics engineering or data engineering teams, preferably in a scaling startup environment

  • 10+ years of total experience in analytics engineering, data engineering, or similar data-focused roles

  • Deep expertise in data modeling, ETL pipelines, and data warehouse architecture

  • Strong technical foundation with expertise in SQL, Python, dbt, and modern data stack tools

  • Proven track record of building and leading high-performing teams

  • Experience partnering with Data Science, Product, and Engineering leaders to deliver key product metrics and user behavior insights

  • Demonstrated ability to balance strategic thinking with hands-on technical leadership

  • Strong communication skills with the ability to translate complex technical concepts for diverse audiences

  • Experience scaling analytics functions from early stage to maturity in rapidly changing environments

  • Track record of establishing data governance, quality standards, and best practices

  • A bias for action and urgency, not letting perfect be the enemy of the effective

  • A "full-stack mindset", not hesitating to do what it takes to solve a problem end-to-end

  • A passion for Anthropic's mission of building helpful, honest, and harmless AI

Apply