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Phd In Statistics Jobs in California (NOW HIRING)

Apex Informatics

Data Scientist

Pleasanton, CA · On-site

Experience manipulating data sets and building statistical models, has a Master's or PHD in Statistics, Mathematics, Computer Science or another quantitative field, and is familiar with the following ...

Brilent

Data Scientist Intern

San Mateo, CA · On-site

Pursuing BS, MS or PhD in Statistics, Applied Mathematics, Computer Science or another quantitative discipline. Advanced degree preferred * Proficient in SQL and able to write complex SQL queries

Mercor

Data Science Expert - AI/ML

San Francisco, CA · Remote

$60 - $90/hr

MSc or PhD in statistics, data science, or a quantitative STEM field, or equivalent experience. * 1+ years in a research, research-engineering, or heavy data-analysis role. * Proficiency in Python ...

New

Genentech

Senior Statistician

South San Francisco, CA

$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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Phd In Statistics information

What is the difference between Phd In Statistics vs Data Scientist?

AspectPhd In StatisticsData Scientist
Required CredentialsTypically a PhD in Statistics or related fieldOften a bachelor's or master's degree in a quantitative field; some roles prefer a PhD
Work EnvironmentAcademic, research institutions, or specialized analytics teamsCorporate, tech companies, or consulting firms
Industry UsageResearch, academia, government, and industry R&DBusiness analytics, product development, and data-driven decision making
Common Search & ComparisonYesYes

While a Phd In Statistics focuses on advanced research, theoretical development, and academic roles, Data Scientists apply statistical and machine learning techniques to solve practical business problems. Both roles require strong analytical skills, but Data Scientists often work in more applied, industry-focused environments, whereas PhD holders may pursue research or academic careers.

What can you 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 to solve complex problems. These roles typically require strong analytical abilities and knowledge of statistical methods and tools like R, Python, or SAS.

Is a PhD in statistics worth it?

A PhD in statistics can lead to advanced roles in research, data science, and academia, often requiring strong analytical and programming skills. 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 are popular job titles related to Phd In Statistics jobs in California?

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

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

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

What cities in California are hiring for Phd In Statistics jobs?

Cities in California with the most Phd In Statistics job openings:

Phd In Statistics jobs near you
Infographic showing various Phd In Statistics job openings in California as of August 2026, with employment types broken down into 100% Full Time. Highlights an 71% In-person, and 29% Remote job distribution.

Data Scientist

Apex Informatics

Pleasanton, CA • On-site

Contractor

Re-posted 26 days ago

Job description

Job Details: Data Scientist
Location: Pleasanton, CA
Top Skill:
Qualifications for Data Scientist Strong problem solving skills with an emphasis on product development.
Experience using statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets.
Experience working with and creating data architectures.
Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages drawbacks.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests anritaprikhodkod proper usage, etc.) and experience with applications.
Experience manipulating data sets and building statistical models, has a Master's or PHD in Statistics, Mathematics, Computer Science or another quantitative field, and is familiar with the following software tools: Coding knowledge and experience with several languages: C, C++, Java, JavaScript, etc.
Knowledge and experience in statistical and data mining techniques: GLM Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc
Experience querying databases and using statistical computer languages: R, Python, SLQ, etc. Experience using web services: Redshift, S3, Spark, , etc.
Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc
Experience analyzing data from 3rd party providers: Client Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Client Insights, etc. Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc. Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.
Top Daily Responsibilities:
1. Support Data-Science and other analytics as needed.
2. Develop SQL queries and data sets 3. Develop business and client facing reports
Skills a Top Candidate Should Have:
  • Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
  • Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
  • Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.
  • Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
  • Experience analyzing data from 3rd party providers: Client Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Client Insights, etc.
  • Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
  • Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.

Desired Skills:
  • Strong problem solving skills with an emphasis on product development.
  • Experience using statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets.
  • Experience working with and creating data architectures.
  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
  • Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
  • Excellent written and verbal communication skills for coordinating across teams.
  • A drive to learn and master new technologies and techniques.
  • We're looking for someone with experience manipulating data sets and building statistical models, has a Master's or PHD in Statistics, Mathematics, Computer Science or another quantitative field, and is familiar with software.

Skills:
1. Excellent Communication Skills.
2. Ability to work with business to gather report requirements.
3. Team player. Custom Job Description: If you have a custom job description that you would like to use. Please paste it here: Knowledge and experience with large data sets, event streams and distributed computing (Hive,Impala,Hadoop etc.) Ability to gather requirements and develop reports in tool selected by business and KPIT.

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