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Data Science Machine Learning Jobs in California

Molina Healthcare

Data Scientist

Long Beach, CA

Leverage their expertise in data science, machine learning, and AI technologies to derive insights from large datasets and create predictive models that drive business decisions. Job Duties Data ...

Molina Healthcare

Data Scientist

Long Beach, CA · On-site +1

$79K - $172K/yr

Leverage their expertise in data science, machine learning, and AI technologies to derive insights from large datasets and create predictive models that drive business decisions. Job Duties • Data ...

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

San Bruno, CA · On-site

$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

Director, Data Science

San Bruno, CA

$169K - $338K/yr

The Data Science team leads advanced analytics and machine learning initiatives to deliver ... impactful business solutions. This group collaborates across functions to translate complex data ...

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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 Jun 29, 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.

Which has more salary, CS or AI?

Data Science and Machine Learning roles in AI generally have higher salaries than traditional computer science positions due to specialized skills in deep learning, neural networks, and advanced algorithms. AI roles often require expertise in programming languages like Python and frameworks such as TensorFlow, which are highly valued in the job market. Salaries vary by experience, location, and industry, but AI-focused positions tend to offer higher compensation on average.

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 engineers make $500,000?

Senior data science and machine learning engineers with extensive experience, advanced skills in programming, statistical analysis, and deep learning, and often working in high-demand industries or at large tech companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at executive or specialized levels.

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 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.

Do data scientists work with machine learning?

Data scientists often work with machine learning as a core part of their role, developing models to analyze data and make predictions. They use tools like Python, R, and libraries such as scikit-learn or TensorFlow to build and deploy machine learning algorithms. Knowledge of statistics, programming, and data manipulation is essential for this work.

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.

Which 3 jobs will survive AI?

Data science and machine learning roles are expected to persist as they require complex problem-solving, domain expertise, and creativity that AI tools currently cannot fully replicate. Jobs involving strategic decision-making, ethical considerations, and interpersonal skills, such as data analysts, AI ethics specialists, and AI system trainers, are also likely to remain in demand. Continuous learning and proficiency with AI tools will be essential for these roles to adapt and thrive.
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 June 2026, with employment types broken down into 60% Full Time, 36% Part Time, 2% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $121,131 per year, or $58.2 per hour.

Data Scientist - Sunnyvale, CA - W2 Contract

Rootshell Enterprise Technologies, Inc.

Sunnyvale, CA • On-site

Other

Posted 2 days ago

Job description

Job Title: Data Scientist
Location: Sunnyvale, CA
12 Months
W2 Contract
Minimum Qualifications
  • BA or BS degree in statistical analysis, computer science, data science, or related field.
  • 7+ years experience in deploying data science, machine learning, or anomaly detection techniques to solve practical business problems.
  • Outstanding written and verbal communication, with the ability to make complex data science concepts understandable to non-technical audiences.
  • Proficiency in SQL, preferably in Snowflake.
  • Experience with anomaly and outlier detection methods and algorithms.
  • Strong programming skills in Python with experience using packages such as Pandas, NumPy, scikit-learn.
  • Experience in quantitative data analysis, possessing a strong ability to conduct in-depth evaluations of complex issues.
  • Have a creative approach to engineer innovative features and signals into analytical solutions, pushing the boundaries of current tools and methodologies.
  • Applied knowledge of statistical data analysis to perform trend and anomaly identification, predictive modeling, and hypothesis testing.
  • Proven ability to make data-driven, convincing arguments to drive process changes.
  • Demonstrated experience in leading data science projects through all phases including exploratory data analysis, data quality management, modeling, tool deployment, and presentation of results.

Preferred Qualifications
  • Experience with Docker, Kubernetes, Airflow.
  • Experience with front end libraries such as React or Streamlit.
  • Experience with LLMs and Graph Databases.
  • Demonstrated ability to implement, improve, debug, and maintain machine learning models.
  • Highly Proficient in Tableau.

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