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Phd Statistics Jobs in California (NOW HIRING)

Sutter Health

Statistician III

Walnut Creek, CA · On-site

$114K - $172K/yr

Master's degree in Statistics, Biostatistics, Mathematics or a closely related field * PhD candidates are encouraged to apply TYPICAL EXPERIENCE: * 2 years of recent relevant experience. SKILLS AND ...

Genentech

Senior Statistician

South San Francisco, CA · On-site

$136K - $254K/yr

You hold an MSc or PhD in Statistics, Biostatistics, or a closely related quantitative field * You have a minimum of 3 years of relevant experience in clinical trial statistics within a ...

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How much do phd statistics jobs pay per year?

As of Sep 5, 2026, the average yearly pay for phd statistics in California is $90,945.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,409.00 and $118,976.00 per year, depending on experience, location, and employer.

What is a PhD statistics?

A PhD Statistics job typically involves conducting advanced statistical research, developing new methodologies, and applying statistical techniques to solve complex problems in various fields such as healthcare, finance, or technology. Professionals in these roles may work in academia, government, or industry, analyzing data, designing experiments, and publishing findings. They often collaborate with interdisciplinary teams to extract insights from large datasets and improve decision-making processes.

What are the key skills and qualifications needed to thrive in a PhD statistics position?

To thrive as a PhD in Statistics, you need advanced knowledge of statistical theory, data analysis, and research methodologies, typically backed by a doctorate in statistics or a closely related field. Expertise with statistical software such as R, SAS, Python, or MATLAB, along with experience in data management systems, is crucial. Strong problem-solving abilities, clear communication, and the capacity to work both independently and as part of interdisciplinary teams are highly valued soft skills. These qualities enable you to devise rigorous solutions to complex data challenges, effectively collaborate with colleagues, and translate findings for stakeholders.

What are the typical projects or research areas for someone with a PhD in statistics?

A PhD in Statistics often works on projects involving the design and analysis of experiments, predictive modeling, advanced data analytics, and the development of new statistical methodologies. Depending on the industry, these may span sectors like healthcare, finance, technology, or government, requiring collaboration with diverse teams of subject matter experts. The role may also involve publishing research, presenting findings, and supporting organizational decision-making with evidence-based insights. This dynamic environment allows statisticians to solve real-world problems and continuously learn new analytical techniques.

How much does a PhD statistician make?

A PhD statistician typically earns between $80,000 and $150,000 annually, depending on experience, industry, and location. Senior roles or positions in finance, technology, or healthcare can offer higher salaries, especially with specialized skills in statistical programming and data analysis tools.

Is a PhD in statistics worth it?

A PhD in statistics can lead to advanced roles in research, data analysis, and academia, often requiring strong analytical and programming skills in tools like R or Python. While it offers high-level expertise and potential for higher salaries, it also involves significant time and financial investment, and job prospects depend on industry demand and individual specialization.

What can I do with a PhD in statistics?

A PhD in statistics prepares individuals for advanced roles in data analysis, research, and modeling across industries such as healthcare, finance, technology, and government. Graduates often work as data scientists, quantitative analysts, research scientists, or statisticians, utilizing skills in statistical software, programming, and data interpretation. These roles typically require strong analytical abilities and knowledge of statistical methods and tools like R, Python, or SAS.

What are the most commonly searched types of Phd Statistics jobs in California?

The most popular types of Phd Statistics jobs in California are:

What are popular job titles related to Phd Statistics jobs in California?

For Phd Statistics jobs in California, the most frequently searched job titles are:

What job categories do people searching Phd Statistics jobs in California look for?

The top searched job categories for Phd Statistics jobs in California are:

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Phd Statistics jobs near you
Infographic showing various Phd Statistics job openings in California as of August 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 82% Physical, 3% Hybrid, and 15% Remote job distribution, with an average salary of $90,945 per year, or $43.7 per hour.

Machine Learning PhD Student Contributor

Cobalt

Santa Rosa, CA • On-site

Other

Posted 5 days ago

Key responsibilities

  • Produce written reasoning traces on complex ML problems and draft expert reference answers to technical questions.

  • Evaluate model-generated technical content by comparing responses, articulating strengths, and identifying points of failure in reasoning.

  • Assess whether conclusions are supported by derivations, code, or experimental evidence, and contribute to project-specific annotation guidelines and quality standards.

Job description

About the role:

Cobalt is seeking PhD-qualified machine learning researchers with direct experience designing, running, and evaluating original ML research. This opportunity is suited to researchers who have worked in academic ML labs, industry research groups, or frontier lab environments, and who understand how technical claims are established, tested, and supported by evidence.

You may currently work, or have previously worked, as a PhD candidate, Postdoctoral Researcher, Research Scientist, Research Engineer, Applied Scientist, Member of Technical Staff, or in a related role.

You do not need prior experience in data annotation or model evaluation. You must, however, have contributed meaningfully to at least one substantive ML research output, and you must be comfortable reading papers, interpreting experimental results, and judging whether stated conclusions follow from the underlying evidence.


What you'll do:

Depending on the project, you may:

  • Produce written reasoning traces on hard ML problems, capturing how you reach a solution rather than only the solution itself, and draft expert reference answers to technical questions
  • Author novel problems in your subfield that have verifiable or defensible correct answers
  • Evaluate model-generated technical content: compare and rank responses, articulate what makes the stronger one stronger, and identify the specific step at which a chain of reasoning breaks down
  • Assess whether stated conclusions are supported by the underlying derivation, code, or experimental evidence
  • Design rubrics and partial-credit criteria for scoring multistep technical tasks, and contribute subject-matter expertise to benchmark and dataset development

Projects follow their own annotation guidelines and quality standards, and you will work with feedback from reviewers and lab research teams.


Required qualifications:

  • PhD, completed or in progress, in machine learning, computer science, statistics, mathematics, physics, or a closely related quantitative discipline, with research that is substantially ML focused
  • Direct experience authoring, co-authoring, or substantively contributing to at least one ML research output, such as a peer-reviewed paper, preprint, thesis chapter, or comparable technical artifact
  • Demonstrated depth in at least one area, for example optimization, reinforcement learning, language model training and post-training, learning theory, probabilistic methods, computer vision, natural language processing, or systems for ML
  • Ability to interpret papers, derivations, code and experimental results, and to explain your reasoning clearly in writing
  • Strong attention to detail, a commitment to factual accuracy, and the ability to work independently to agreed timelines


Why join Cobalt AI:

  • Advance frontier AI where it counts. Apply your research expertise to the data that frontier labs cannot obtain any other way, where your reasoning directly shapes how the next generation of models works through technical problems.
  • Grow professionally. Expand your influence through evaluation projects, advisory roles, and research collaborations, while deepening your understanding of how frontier models are trained and assessed.
  • Work with a top-tier network. Collaborate with researchers from leading institutions and labs on high-impact, flexible work.
  • Set your own schedule. Flexible 10 to 40 hour weeks that fit around your research position and your life.
  • Competitive pay. Rates vary by project and are determined by a number of factors, including scope, skillset, and experience.

Apply