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Data Science Machine Learning Jobs in Walnut Creek, CA

CYNET SYSTEMS

Principal Data Scientist

Oakland, CA · On-site

$128 - $148/hr

Master's Degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field. * Experience in Data Science ...

Match Point Solutions

Data Scientist

Pleasanton, CA · Remote

$75 - $80/hr

Applies data science, machine learning and other analytical modeling methods to develop defensible and reproducible predictive models * Serves as the technical lead for the development of computer ...

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 ...

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 ...

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All JobsData Science Machine Learning JobsData Science Machine Learning Jobs in Walnut Creek, CA

Data Science Machine Learning information

See Walnut Creek, CA salary details

$41.5K

$135.9K

$217.6K

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

As of Aug 12, 2026, the average yearly pay for data science machine learning in Walnut Creek, CA is $135,923.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,100.00 and $150,600.00 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?

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.

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 cities near Walnut Creek, CA are hiring for Data Science Machine Learning jobs? Cities near Walnut Creek, CA 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 Walnut Creek, CA as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 16% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $135,923 per year, or $65.3 per hour.

Principal Data Scientist

CYNET SYSTEMS

Oakland, CA • On-site

$128 - $148/hr

Contractor

Re-posted 6 days ago

Job description

Job Overview:

Pay Range: $128.66hr - $148.45hr

Requirement/Must Have:

  • Master’s Degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field.
  • Experience in Data Science, 8+ years or 2+ years experience if possessing Doctoral Degree or higher in a related field.

Responsibilities:

  • Researches and applies advanced knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions.
  • Creates advanced data mining architectures/models/protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets.
  • Extracts, transforms, and loads data from dissimilar sources for machine learning feature engineering.
  • Applies data science/machine learning/artificial intelligence methods to develop defensible and reproducible predictive or optimization models.
  • Wrangles and prepares data as input for machine learning model development and feature engineering.
  • Architects, develops, and documents reusable functions and modular code for data science.
  • Assesses business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures, and advanced data analysis.
  • Works with stakeholder departments and subject matter experts to understand application and potential of data science solutions.
  • Presents findings and makes recommendations to senior management.
  • Acts as peer reviewer of complex models.

Nice to Have:

  • Doctorate Degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field.
  • Expertise in experimental design and causal inference methods.
  • Expertise in statistical methods for time series analysis, statistical modeling, and probabilistic risk assessment.
  • Relevant industry experience (electric or gas utility, data science consulting, etc.).
  • Familiarity with the use of supervised, unsupervised, deep learning & physics-based methods for modeling electrical infrastructure failure modes.
  • Competency with data science standards and processes (model evaluation, optimization, feature engineering, etc.) along with best practices.
  • Knowledge of industry trends and current issues in job-related area of responsibility.
  • Competency with Agile product development best practices.
  • Proficiency with Python or PySpark, code reviews, and code development best practices.
  • Proficiency in explaining technical concepts including statistical inference, machine learning algorithms, software engineering, model deployment pipelines.
  • Mastery in clearly communicating complex technical details and insights to colleagues and stakeholders.
  • Ability to develop, coach, teach and/or mentor others to meet both their career goals and the organization goals.

Skills:

  • Pyspark proficiency.
  • User interface development proficiency.
  • Strong cross-functional collaboration skills.
 

Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading staffing and recruiting powerhouse. Proudly recognized as a nationally and locally certified diversity firm, Cynet delivers agile, scalable talent solutions across industries. With an active footprint in all 50 U.S. states and Canada, we support thousands of consultants through our expansive, high-performing recruitment engine operating across North America and Asia—ensuring speed, quality, and consistency in every hire.
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About Cynet Systems

Sourced by ZipRecruiter

Cynet Systems Inc is a staffing and recruiting corporation nestled in Ashburn, VA, USA. Established in 2010, the company operates within the Information Technology and Services sector, specializing in providing effective workforce solutions to different business needs, including IT consulting, direct hire, and contract staffing services. Through the years, Cynet Systems has built an impressive portfolio, going beyond borders and expanding its operations internationally in Canada and India. Rooted in its core values of teamwork, leadership, and commitment, Cynet Systems helps businesses unlock their full potential by providing versatile and competent professionals that perfectly align with their needs. Fueled by their unwavering mission to deliver top-tier talent to businesses worldwide, Cynet Systems garnered various recognitions including SIA's fastest-growing staffing firms and Best Place to Work in Virginia for 2019.

Industry

It services

Company size

501 - 1,000 Employees

Headquarters location

Sterling, VA, US

Year founded

2010

Social media

Twitter

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