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Data Science Manager Jobs in California (NOW HIRING)

As a Data Science Manager, you will act as a pivotal technical leader to bridge the gap between complex business questions and advanced technical execution. You will build, mentor, and lead a high ...

We're looking for a Data Science Manager to lead our growing AI product data science function. This is a foundational role focused on building and managing a small team of data scientists working on ...

Data Science Manager

San Francisco, CA · On-site

$160K - $220K/yr

We're looking for a Data Science Manager to lead our growing AI product data science function. This is a foundational role focused on building and managing a small team of data scientists working on ...

We're looking for a Data Science Manager to lead our growing AI product data science function. This is a foundational role focused on building and managing a small team of data scientists working on ...

Data Science Manager

Los Angeles, CA · On-site

$160K - $220K/yr

We're looking for a Data Science Manager to lead our growing AI product data science function. This is a foundational role focused on building and managing a small team of data scientists working on ...

Data Science Manager

San Francisco, CA · On-site

$220K - $330K/yr

We are seeking a Manager, Data Science to lead the data strategy for two critical areas of our business: Treatment Experience and Hinge Select. In this role, you will own the data roadmap for our ...

About the Role As Data Science Manager, Integrity, you will lead a team of data scientists working across trust & safety, fraud prevention, risk analysis, measurement, and modeling. You'll be ...

To strengthen our efforts, we are hiring a Data Science Manager who will lead data scientists and data analysts helping us to make data driven decisions. Data Science & Analytics is at the heart of ...

To strengthen our efforts, we are hiring a Data Science Manager who will lead data scientists and data analysts helping us to make data driven decisions. Data Science & Analytics is at the heart of ...

The Manager of Data Science for GTM is a hands-on player coach who leads from the front across Sales, Customer Success, and Marketing analytics. They are deeply technical, opinionated about the GTM ...

The Manager of Data Science for GTM is a hands-on player coach who leads from the front across Sales, Customer Success, and Marketing analytics. They are deeply technical, opinionated about the GTM ...

The role involves managing a team of Data Scientists to support lending businesses by leveraging data to improve execution and deliver impactful projects. Responsibilities : • Manage a team of Data ...

The role The Borrow Data Science team is seeking a Manager who will help support growth in our Lending businesses by improving SoFi's ability to execute with data. This is an exciting role for ...

We're looking for a Senior Data Science Manager to lead the data science powering Mercury's revenue and product value engine. This team supports Go-To-Market functions across Finance, Marketing, and ...

... manage high performance, responsive team of data scientists and analysts that are able to not only keep up with but also pioneer in this space 3. Build and prototype analysis pipelines for the team ...

In this hybrid role you will report to a Data Science Manager. You will: * Develop evaluation frameworks for autonomous vehicle performance, for large-scale ML models, and for the quality of ...

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Showing results 1-20

Data Science Manager information

See California salary details

$30.6K

$95.9K

$169.7K

How much do data science manager jobs pay per year?

As of Jul 5, 2026, the average yearly pay for data science manager in California is $95,873.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,100.00 and $123,900.00 per year, depending on experience, location, and employer.

What are the primary responsibilities of a Data Science Manager on a day-to-day basis?

As a Data Science Manager, your daily responsibilities typically include overseeing a team of data scientists and analysts, setting project priorities, and ensuring the timely delivery of data-driven solutions. You will often collaborate with cross-functional teams, such as engineering, product, and business stakeholders, to define problems, scope solutions, and communicate analytical insights. Your role also involves mentoring team members, reviewing code and analysis, and driving best practices in data science methodologies. This position requires balancing technical project oversight with team leadership and strategic business alignment.

What is a Data Science Manager job?

A Data Science Manager leads a team of data scientists to develop and implement data-driven solutions for business challenges. They oversee project timelines, ensure the quality of data analysis, and collaborate with cross-functional teams to drive decision-making. In addition to technical expertise, they require strong leadership, communication, and strategic thinking skills. Their role bridges the gap between data science initiatives and business objectives, ensuring the team's work aligns with company goals.

Is 40 too late for data science?

Age is not a barrier to becoming a data science manager; many professionals transition into data science roles later in their careers. Success depends on relevant skills, experience, and continuous learning in areas like programming, statistics, and machine learning. Employers value diverse backgrounds and practical expertise regardless of age.

What is the 80 20 rule in data science?

The 80/20 rule in data science suggests that roughly 80% of results come from 20% of the efforts or data. Data scientists often use this principle to focus on the most impactful features, models, or data subsets to improve efficiency and outcomes in projects.

What is the role of a data science manager?

A data science manager oversees data science teams, guiding project priorities, setting strategic goals, and ensuring the effective use of data analysis and modeling techniques. They coordinate between technical staff and business stakeholders, often requiring skills in leadership, communication, and familiarity with tools like Python, R, or SQL. Their responsibilities include managing workflows, mentoring team members, and ensuring project deliverables align with organizational objectives.

How much do data scientist managers make?

Data Science Managers typically earn between $110,000 and $160,000 annually, with salaries varying based on experience, location, and company size. They often oversee teams of data scientists, coordinate projects, and require strong skills in analytics tools and leadership. Senior roles or those in high-cost areas can offer higher compensation.

What are the key skills and qualifications needed to thrive in the Data Science Manager position, and why are they important?

To thrive as a Data Science Manager, you need strong analytical skills, experience in machine learning and data analytics, and a background in statistics or computer science, often supported by an advanced degree. Familiarity with tools like Python, R, SQL, cloud platforms, and experience managing data science projects are highly valued, and certifications such as Certified Analytics Professional (CAP) can be advantageous. Excellent leadership, project management, and communication skills are crucial for guiding teams and translating technical findings for stakeholders. These abilities ensure effective team performance, successful project delivery, and the alignment of data science initiatives with organizational goals.

What are the most commonly searched types of Data Science jobs in California? The most popular types of Data Science jobs in California are:
What job categories do people searching Data Science Manager jobs in California look for? The top searched job categories for Data Science Manager jobs in California are:
What cities in California are hiring for Data Science Manager jobs? Cities in California with the most Data Science Manager job openings:
Infographic showing various Data Science Manager job openings in California as of June 2026, with employment types broken down into 100% Full Time. Highlights an 80% In-person, and 20% Remote job distribution, with an average salary of $95,873 per year, or $46.1 per hour.
Data Science Manager

Data Science Manager

Tapestry

Mountain View, CA • On-site

Other

Medical, Dental, Vision, Retirement, PTO

Posted 7 days ago


Tapestry Inc. rating

8.0

Company rating: 8.0 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

1st of 102 rated fashion retailers


Job description

About Tapestry

Tapestry is a team within Alphabet working to build the AI-powered electric grid. We are tackling one of the world's most important infrastructure challenges: helping the energy system become more visible, understandable, reliable, affordable, abundant, and clean.

Originally born at X, Alphabet's moonshot factory, Tapestry brings together experts in energy, AI, software, engineering, and product to build tools that help the electricity ecosystem plan smarter, move faster, and operate more efficiently.

This is a global effort. Tapestry supports partners across the U.S., U.K., Chile, New Zealand, Australia, and Brazil as they work toward a cleaner, more resilient energy future.

Joining Tapestry means doing high-impact work with a multidisciplinary team tackling a problem that matters at global scale. Learn more about our team and our mission here.

About the role:

At Tapestry, data drives all our decision-making. Data Scientists work across the organization to help shape our business and technical strategies by processing, analyzing, and interpreting massive datasets. They lead our metrics assessment, analyze massive datasets and derive early insights, and partner with cross functional teams on the right datasets for maximum downstream impact. As a Data Science Manager, you will act as a pivotal technical leader to bridge the gap between complex business questions and advanced technical execution. You will build, mentor, and lead a high-performing team of data scientists to deliver operational excellence, accelerate product advancement, and drive business value.

In this role, you will deeply immerse yourself with the team of data scientists in data collection and analysis, develop compelling, synthesized recommendations for senior leadership, and be involved  to help drive implementation. Ultimately, your team's solutions will fundamentally improve electric grid visibility and resilience.

How you will contribute to the team...

1. Team Leadership and Strategic Delivery

  • People Management: Recruit, mentor, and lead a world-class team of data scientists. Cultivate talent through active technical mentorship and clear career development paths.
  • Cross-Functional Alignment: Collaborate with engineering, product, power system experts, and external partners to translate high-level business goals into rigorous data science roadmaps.
  • Executive Communication: Persuasively communicate your team's findings and strategic recommendations to senior executives and cross functional teams, tracking the long-term business impact of the solutions.

2. Data Integrity and Curation Strategy at Scale

  • Pipeline Oversight: Guide the team in discovering, investigating, and deriving insights from large and complex input datasets, both current and potential, from partners and other sources.
  • Gatekeeping Metrics: Oversee the definition of problem framing, test datasets, and core business, product and performance metrics that machine learning models will aim to optimize for.
  • Multi-Stage Quality Control: Ensure data integrity across the pipeline by establishing frameworks to assess intermediate datasets and metrics within multi-stage machine learning processes.
  • Annotation Rigor: Drive a comprehensive and scalable data annotation strategy that prioritizes quality through statistical rigor, ensuring data reliability for all downstream modeling.

3. Problem Definition and Advanced Analytics

  • Grid Visibility and Innovation: Lead the proactive exploration of new problem spaces to fundamentally improve electric grid visibility and resilience.
  • Experimentation Frameworks: Standardize how the team designs, executes, and analyzes A/B tests and other experiments to validate hypotheses and measure product impact.
  • Engineering Best Practices: Champion modern data science workflows, including the application of GenAI techniques for data analysis, ensuring the team follows robust engineering best practices.
What you should have...
  • PhD or Master's in a quantitative field and 8+ years of tech or energy industry work experience as a statistician, quantitative analyst, or data scientist. 
  • 5+ years of experience directly managing or leading high-performing data science and analytics teams, with a proven track record of delivering production-grade data solutions.
  • A proven track record of identifying where data science can add unique value during early product development, alongside a strong ability to influence other teams to collaborate on critical data science work.
  • Advanced skills in experimental design, including the ability to architect, guide, and validate robust A/B testing methodologies and statistical experiments in ambiguous environments.
  • Experience in Python, SQL, R, Pandas, Scikit-Learn, other ML frameworks as appropriate.
  • Experience with electric power grid data, and physics based understanding of electrical networks and utility data. Ability to bridge the gap between power systems and machine learning.
  • Experience in multivariate analysis, stochastic models, and sampling methods. Able to select the right statistical tool to solve for bias, variance, and data drift.
  • Applied experience with building comprehensive machine learning model evaluation tooling and processes on large datasets.
  • Proven ability to "zoom out" from complex technical details to build a cohesive product strategy, and "zoom in" to unblock technical hurdles.
  • Demonstrate strong collaboration with software engineering and cross functional teams to build ML-powered systems ready for production.
  • Exceptional storytelling abilities, with a knack for turning complex data pipelines and model metrics into clear business value for non-technical stakeholders.
Would be great to have...
  • Ability to thrive in ambiguity, set own goals and effectively delivering to them in a very fast-changing environment
  • Attention to detail, project management, and organizational skills
  • Fast learner with capacity to learn about a wide-spread of different technologies and industries
  • Passion for the energy and climate space
  • Track record of delivering scalable solutions to complex software problems
  • Experience in startup or high-growth environments

Tapestry Values:

  • Take charge: We take initiative and own outcomes that move the mission forward.
  • Transform with purpose: We build solutions that solve real problems and create meaningful impact.
  • Be a Tapestry, not a thread: We collaborate across diverse skills and perspectives to achieve more than we can individually.
  • Always fine-tune: We stay curious, seek feedback, and refine our understanding as we learn.
  • Stay grounded: We listen openly, value different perspectives, and stay focused on what matters most.

What we offer:

A culture that supports growth, ownership, and meaningful impact, along with...

  • Competitive salary and equity
  • Medical, dental, and vision coverage
  • Generous PTO and flexible hybrid work model
  • 401(k) with employer contribution
  • Professional development
  • The ability to work on important real-world problems within an Alphabet-backed environment

The US base salary range for this full-time position is $207,000 - $304,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.


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