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Data Science Machine Learning Jobs in California

IQUASAR LLC

DATA SCIENTIST

San Diego, CA · On-site

Completed coursework in Machine Learning, Deep Learning, Algorithm Design, and Data Science * Knowledge of Python libraries such as Pandas, scikit-learn, Keras, TensorFlow, and/or PyTorch * Good ...

iQuasar LLC

DATA SCIENTIST

San Diego, CA · On-site

$120K - $150K/yr

Completed coursework in Machine Learning, Deep Learning, Algorithm Design, and Data Science * Knowledge of Python libraries such as Pandas, scikit-learn, Keras, TensorFlow, and/or PyTorch * Good ...

Find Data Science

Data Scientist

San Francisco, CA

Your expertise in data analysis, machine learning, and statistical modeling will enable you to ... Stay updated on industry trends and best practices regarding data science methodologies and ...

Qualcomm

Data Scientist, Staff

San Diego, CA · On-site

$142.10 - $213.10/hr

Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or related field. * 5+ years of Data Science or related work experience. * Completed advanced degrees ...

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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 Sep 2, 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 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 are the key skills and qualifications needed to thrive as a data science machine learning professional?

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 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.

Is data science machine learning a high paying job?

Data science and machine learning roles are generally high-paying within the tech industry due to the specialized skills required, such as programming, statistical analysis, and experience with tools like Python or TensorFlow. Salaries vary based on experience, location, and company size but tend to be above average compared to many other professions.

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 August 2026, with employment types broken down into 1% As Needed, 79% Full Time, 15% Part Time, and 5% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% 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

Re-posted 7 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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