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Data Science Machine Learning Jobs in California

Apogee Global RMS

Senior Data Scientist

San Jose, CA · On-site +1

Bachelor's or Master's in Data Science, Computer Science, Statistics, Mathematics, or related field. * 3+ years of experience in data science, machine learning, or advanced analytics. * Strong ...

Apogee Global RMS

Senior Data Scientist

San Jose, CA · On-site +1

Bachelor's or Master's in Data Science, Computer Science, Statistics, Mathematics, or related field. * 3+ years of experience in data science, machine learning, or advanced analytics. * Strong ...

Walmart

Senior, Data Scientist

South San Francisco, CA

$117K - $234K/yr

Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful ...

Walmart

Senior, Data Scientist

Hayward, CA

$117K - $234K/yr

Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful ...

Walmart

Senior, Data Scientist

Castro Valley, CA

$117K - $234K/yr

Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful ...

Walmart

Senior, Data Scientist

San Bruno, CA

$117K - $234K/yr

Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful ...

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All JobsData Science Machine Learning JobsData Science Machine Learning Jobs in California

Data Science Machine Learning information

See California salary details

$37K

$121.1K

$193.9K

How much do data science machine learning jobs pay per year?

As of May 30, 2026, the average yearly pay for data science machine learning in California is $121,131.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,200.00 and $134,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Data Science Machine Learning professional, and why are they important?

To thrive as a Data Science Machine Learning professional, you need a strong background in statistics, programming (usually Python or R), and a solid understanding of machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with tools like TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications such as AWS Certified Machine Learning, are typically valuable. Critical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These skills enable professionals to develop robust models, extract actionable insights, and drive data-driven decision-making in organizations.

What are some common challenges faced when deploying machine learning models as a Data Science Machine Learning professional?

A frequent challenge in this role is bridging the gap between building accurate models in a controlled environment and deploying them effectively in production systems. Issues such as data drift, model performance degradation, and integration with existing IT infrastructure often arise. Collaboration with engineering and IT teams is crucial to ensure models are scalable, maintainable, and secure. Regular monitoring and updating of deployed models are also essential responsibilities to sustain their value to the business.

What is data science machine learning?

Data science machine learning refers to the use of algorithms and statistical models to analyze and draw insights from complex data sets. In this field, professionals use machine learning techniques to build predictive models, automate decision-making processes, and uncover patterns in data. Machine learning is a core component of data science, enabling systems to improve their performance over time without being explicitly programmed. Data scientists with machine learning expertise are in high demand across industries like healthcare, finance, and technology.

What is the difference between Data Science Machine Learning vs Data Analyst?

AspectData Science Machine LearningData Analyst
Required SkillsProgramming (Python, R), statistics, machine learning algorithmsData visualization, SQL, basic statistics
Work EnvironmentDeveloping models, coding, experimenting with algorithmsData reporting, dashboard creation, data cleaning
Industry UsageTech, finance, healthcare, where predictive models are neededBusiness intelligence, marketing, operations

Data Science Machine Learning professionals focus on building predictive models and algorithms using programming and advanced statistics, often working on complex projects. Data Analysts primarily interpret data through visualization and reporting to support business decisions. While both roles require data skills, Data Science Machine Learning involves more technical programming and modeling, whereas Data Analysts focus on data interpretation and presentation.

What are popular job titles related to Data Science Machine Learning jobs in California? For Data Science Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Data Science Machine Learning jobs in California look for? The top searched job categories for Data Science Machine Learning jobs in California are:
What cities in California are hiring for Data Science Machine Learning jobs? Cities in California with the most Data Science Machine Learning job openings:
Data Science Machine Learning jobs near you
Infographic showing various Data Science Machine Learning job openings in California as of May 2026, with employment types broken down into 50% Internship, and 50% Full Time. Highlights an 100% In-person job distribution, with an average salary of $121,131 per year, or $58.2 per hour.
AI/Machine Learning Engineering - Intern

AI/Machine Learning Engineering - Intern

DataVisor

Mountain View, CA

$25 - $70/hr

Contractor

Posted 8 days ago

Job description

About DataVisor

DataVisor is the world's leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!

Role Summary

We are seeking highly motivated, soon-to-graduate MS or Ph.D. students in Computer Science, Machine Learning, Data Science, or related fields to join us as AI / ML Engineering Interns.

This internship is ideal for candidates who are eager to learn how large-scale AI systems are built and deployed in production. You will work closely with experienced engineers and data scientists to help build the Intelligence Layer and Data Consortium that power DataVisor's real-time fraud detection platform. 

This internship focuses on distributed systems, data pipelines, machine learning infrastructure, and applied AI, including exposure to agentic flows and large language models (LLMs).

What You'll Do

  • Data Engineering & Pipelines
    • Assist in building and maintaining high-throughput data pipelines using technologies such as Spark, Kafka, or Flink
    • Help process and aggregate real-time signals (e.g., device fingerprints, behavioral data) into shared intelligence systems
  • Distributed Systems & Scalability
    • Learn to design and optimize backend systems that support large-scale, real-time decisioning
    • Contribute to improving system performance, reliability, and latency under high transaction volumes
  • AI Applications & Agentic Flows
    • Support the development of AI applications and agentic workflows using state-of-the-art LLMs (e.g., OpenAI, Anthropic, Google)
    • Experiment with natural language interfaces, intelligent rule suggestions, and automated strategy generation
  • Machine Learning Pipelines
    • Help deploy and monitor pipelines for unsupervised and supervised ML models
    • Assist with integrating models into real-time scoring APIs and decision engines
  • Privacy & Security
    • Learn best practices for privacy-first system design, including tokenization and hashing to protect sensitive data
  • Cross-Functional Collaboration
    • Work alongside Data Science, Product, and Engineering teams to test ideas, validate models, and ship production features

Requirements

  • Current MS or Ph.D. students majoring in Computer Science, Machine Learning, AI, Data Science, or a related field 
  • Passionate about learning how real-world AI systems are built at scale
  • Comfortable working with complex technical problems and eager to grow through mentorship
  • Strong programming skills in Python
  • Familiarity with at least one of the following: distributed systems, machine learning, data engineering, or backend development
  • Academic or project experience with big data frameworks (Spark, Kafka, Flink) is a plus
  • Understanding of core ML concepts (supervised / unsupervised learning)
Preferred (Nice-to-Have)
  • Coursework or project experience with:
    • LLMs, RAG architectures, LangChain, or vector databases
    • Cloud platforms (AWS) and containers (Docker)
    • Stream processing or real-time systems
  • Interest in fraud, risk, or security domains (not required)

Benefits

  • Hands-on experience working on production-scale AI systems
  • Mentorship from senior engineers and data scientists
  • Exposure to cutting-edge agentic AI and LLM applications
  • Opportunity for full-time conversion based on performance and business needs
  • Comp Range, $25 - $70/hour

Apply