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Data Science Jobs in Riverside, CA (NOW HIRING)

Data Scientist II

Irvine, CA · On-site +1

$82K - $127K/yr

Translate business and operational needs into scalable data science solutions and modeling approaches * Perform feature engineering, data preparation, and exploratory analysis to support model ...

Translate business and operational needs into scalable data science solutions and modeling approaches * Perform feature engineering, data preparation, and exploratory analysis to support model ...

AbbVie Data Science is the best-in-class team within its cross-industry peer group and is responsible for bringing people, process, and technology together to generate business value from clinical ...

AbbVie Data Science is the best-in-class team within its cross-industry peer group and is responsible for bringing people, process, and technology together to generate business value from clinical ...

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Data Science information

See Riverside, CA salary details

$39.1K

$128K

$205K

How much do data science jobs pay per year?

As of Jul 5, 2026, the average yearly pay for data science in Riverside, CA is $128,049.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,800.00 and $141,900.00 per year, depending on experience, location, and employer.

Is data science a good career?

Data science is a growing field with high demand for professionals skilled in statistics, programming, and data analysis tools like Python and R. It offers competitive salaries, diverse industry applications, and opportunities for advancement, making it a strong career choice for those with relevant skills and education.

What are the key skills and qualifications needed to thrive as a Data Scientist, and why are they important?

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

Is 40 too late for data science?

Data science is a field open to individuals of all ages, and many professionals transition into it later in their careers. Success often depends on acquiring relevant skills such as programming, statistics, and machine learning, which can be learned through online courses, bootcamps, or degrees regardless of age.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What jobs can a Data Scientist do?

A Data Scientist can work in roles such as data analyst, machine learning engineer, data engineer, or business intelligence analyst. These roles involve analyzing large datasets, developing predictive models, and using tools like Python, R, and SQL to support decision-making across various industries.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

What work do you do as a Data Scientist?

A Data Scientist analyzes large datasets to extract insights, build predictive models, and inform business decisions. They use programming languages like Python or R, and tools such as SQL and machine learning frameworks, often working in collaborative environments with data engineers and analysts.

What Does a Data Scientist Do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.
What are the most commonly searched types of Data Science jobs in Riverside, CA? The most popular types of Data Science jobs in Riverside, CA are:
What are popular job titles related to Data Science jobs in Riverside, CA? For Data Science jobs in Riverside, CA, the most frequently searched job titles are:
What job categories do people searching Data Science jobs in Riverside, CA look for? The top searched job categories for Data Science jobs in Riverside, CA are:
What cities near Riverside, CA are hiring for Data Science jobs? Cities near Riverside, CA with the most Data Science job openings:
Infographic showing various Data Science job openings in Riverside, CA as of June 2026, with employment types broken down into 1% As Needed, 83% Full Time, 13% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $128,049 per year, or $61.6 per hour.
Data Scientist II

Data Scientist II

Corvel

Irvine, CA • On-site, Remote

$82K - $127K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 28 days ago


CorVel rating

7.9

Company rating: 7.9 out of 10

Based on 51 frontline employees who took The Breakroom Quiz

82nd of 146 rated financial services


Job description

We have an exciting opportunity for a Data Scientist within our data product space. This individual will be focused on designing, building, and deploying machine learning models and data products that support our enterprise initiatives. This role focuses on developing scalable, production ready solutions by translating complex business problems into data-driven approaches and model-based outputs.
Working closely with product managers, engineering teams, and business stakeholders, this position contributes to the development of data products from concept through deployment, ensuring solutions are reliable, performant, and aligned with real world use cases. The role includes hands on model development, feature engineering, and integration into production systems within cloud environments.
The ideal candidate has experience building and operationalizing machine learning models and is comfortable working with modern AI techniques, including large language models (LLMs) and retrieval-augmented generation (RAG), where applicable. Experience with platforms such as Azure, AWS, or similar ecosystems is strongly preferred.
Success in this role requires strong technical expertise, problem-solving skills, and the ability to deliver high-quality solutions within a structured development environment. This role focuses on building and deployment of production data products and is not limited to exploratory analysis or reporting.
This position is open to remote or hybrid.
ESSENTIAL FUNCTIONS & RESPONSIBILITIES:
  • Mine and analyze data from internal databases to drive optimization and improvement of product development and business strategies
  • Creating new, experimental frameworks to collect data
  • Building tools to automate data collection
  • Develop custom data models and algorithms to apply to data sets
  • Design, build, train, and deploy machine learning models and data products for enterprise use
  • Translate business and operational needs into scalable data science solutions and modeling approaches
  • Perform feature engineering, data preparation, and exploratory analysis to support model development
  • Develop and evaluate models using appropriate techniques (e.g., classification, regression, NLP, optimization)
  • Contribute to the design of data products, including model outputs, APIs, and integration into downstream systems
  • Support advanced AI use cases, including LLM-based solutions, retrieval-augmented generation (RAG), and hybrid modeling approaches where appropriate
  • Collaborate with engineering teams to integrate models into production environments using APIs, pipelines, and cloud services
  • Support deployment and lifecycle management of models within Azure Machine Learning, AWS, or similar platforms
  • Perform model validation, testing, and documentation to ensure quality and reproducibility
  • Contribute to technical design discussions and provide input on architecture and implementation strategies
  • Work within the full software development lifecycle (SDLC), including version control, testing, and release processes
  • Communicate model behavior, assumptions, and results clearly to technical and non-technical stakeholders
  • Develop A/B testing framework and test model quality
  • Passion for technology and emerging AI/ML trends
  • Additional duties as assigned

KNOWLEDGE & SKILLS:
  • Strong problem-solving skills with an emphasis on product development.
  • Strong foundation in machine learning, statistical modeling, and data science techniques
  • Experience building and deploying machine learning models in production environments
  • Familiarity with modern AI approaches, including:
    • Natural language processing (NLP)
    • Large language models (LLMs)
    • Retrieval-Augmented Generation (RAG)
    • Feature engineering and model evaluation techniques
  • Experience working with cloud platforms such as Azure, AWS, or similar ecosystems
  • Familiarity with data pipelines, APIs, and integration patterns
  • Proficiency in programming languages such as Python and database management including SQL
  • Strong problem-solving skills with the ability to structure complex problems into analytical solutions
  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.), their real-world advantages/drawbacks and experience with applications
  • Excellent presentation and written/verbal communication skills

EDUCATION & EXPERIENCE:
  • Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field; Master's preferred
  • 2-5+ years of experience in data science, machine learning, or related roles
  • Experience developing and deploying machine learning models in production environments
  • Experience with cloud-based ML platforms such as Azure Machine Learning, AWS SageMaker, or similar
  • Exposure to AI platforms such as Azure OpenAI, AWS Bedrock, or similar technologies preferred
  • Experience working within enterprise software environments and SDLC practices preferred

PAY RANGE:
CorVel uses a market based approach to pay and our salary ranges may vary depending on your location. Pay rates are established taking into account the following factors: federal, state, and local minimum wage requirements, the geographic location differential, job-related skills, experience, qualifications, internal employee equity, and market conditions. Our ranges may be modified at any time.
For leveled roles (I, II, III, Senior, Lead, etc.) new hires may be slotted into a different level, either up or down, based on assessment during interview process taking into consideration experience, qualifications, and overall fit for the role. The level may impact the salary range and these adjustments would be clarified during the offer process.
Pay Range: $82,574 - $127,490
A list of our benefit offerings can be found on our CorVel website: CorVel Careers | Opportunities in Risk Management
In general, our opportunities will be posted for up to 1 year from date of posting, or until we have selected candidate(s) to fulfill the opening, whichever comes first.
About CorVel
CorVel, a certified Great Place to Work® Company, is a national provider of industry-leading risk management solutions for the workers' compensation, auto, health and disability management industries. CorVel was founded in 1987 and has been publicly traded on the NASDAQ stock exchange since 1991. Our continual investment in human capital and technology enable us to deliver the most innovative and integrated solutions to our clients. We are a stable and growing company with a strong, supportive culture and plenty of career advancement opportunities. Over 4,000 people working across the United States embrace our core values of Accountability, Commitment, Excellence, Integrity and Teamwork (ACE-IT!).
A comprehensive benefits package is available for full-time regular employees and includes Medical (HDHP) w/Pharmacy, Dental, Vision, Long Term Disability, Health Savings Account, Flexible Spending Account Options, Life Insurance, Accident Insurance, Critical Illness Insurance, Pre-paid Legal Insurance, Parking and Transit FSA accounts, 401K, ROTH 401K, and paid time off.
CorVel is an Equal Opportunity Employer, drug free workplace, and complies with ADA regulations as applicable.
#LI-Remote
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights notice from the Department of Labor.

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