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Data Science Degree Jobs in Riverside, CA (NOW HIRING)

FieldAI

Field Data Analyst

Irvine, CA · On-site

Required : • Bachelor's degree in Engineering, Data Science, Computer Science, Statistics, or related field • Strong analytical skills with ability to work with messy operational datasets • ...

FieldAI

Field Data Analyst

Irvine, CA · On-site

Required : • Bachelor's degree in Engineering, Data Science, Computer Science, Statistics, or related field • Strong analytical skills with ability to work with messy operational datasets • ...

PM2CM

Senior Data Analyst

Pomona, CA · On-site

$86K - $109K/yr

Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, or a related field. * Minimum of 5 years of experience in data analysis, preferably in a project management or business ...

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All JobsData Science Degree JobsData Science Degree Jobs in Riverside, CA

Data Science Degree information

See Riverside, CA salary details

$39.1K

$128K

$205K

How much do data science degree jobs pay per year?

As of Aug 15, 2026, the average yearly pay for data science degree 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.

What is the difference between Data Science Degree vs Data Analyst?

AspectData Science DegreeData Analyst
Required CredentialsBachelor's or Master's in Data Science, Computer Science, or related fieldsBachelor's in Statistics, Mathematics, or related fields
Work EnvironmentResearch, modeling, developing algorithms, often in tech or finance industriesData cleaning, reporting, visualization, supporting business decisions
Employer & Industry UsageTech companies, finance, healthcare, academiaRetail, marketing, finance, healthcare

Data Science Degree programs focus on advanced analytics, machine learning, and programming, preparing individuals for complex modeling roles. Data Analysts typically handle data processing, visualization, and reporting to support decision-making. While both roles require strong analytical skills, Data Science degrees emphasize technical and statistical expertise, whereas Data Analysts focus on interpreting data for business insights.

What is a data science degree?

A Data Science degree is an academic program that prepares students to analyze, interpret, and derive insights from complex data sets using statistical, computational, and machine learning techniques. The curriculum typically includes coursework in mathematics, statistics, computer science, and specialized data science methods. Graduates are equipped with the skills to work in a variety of industries, tackling real-world problems by leveraging data-driven decision making. This degree can be pursued at the undergraduate or graduate level, and often includes hands-on projects and internships.

What types of real-world projects or team collaborations can I expect to work on after earning a data science degree?

After earning a Data Science degree, you can expect to engage in a variety of real-world projects such as building predictive models, analyzing large datasets to uncover business insights, and developing data-driven solutions for organizational challenges. Data scientists often collaborate closely with cross-functional teams, including software engineers, business analysts, and domain experts, to translate complex data findings into actionable strategies. These collaborations not only enhance your technical skills but also provide valuable experience in communication and project management, which are essential for career growth in this field.

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 mathematics, statistics, and programming, typically supported by a degree in data science, computer science, or a related field. Proficiency in technical tools such as Python or R, SQL, and machine learning frameworks, along with relevant certifications, is highly valued. Strong problem-solving abilities, effective communication, and curiosity help set apart top-performing data scientists. These skills are crucial for extracting actionable insights from data, collaborating with stakeholders, and driving data-driven decision-making.

What are popular job titles related to Data Science Degree jobs in Riverside, CA?

For Data Science Degree jobs in Riverside, CA, the most frequently searched job titles are:

What job categories do people searching Data Science Degree jobs in Riverside, CA look for?

The top searched job categories for Data Science Degree jobs in Riverside, CA are:

What cities near Riverside, CA are hiring for Data Science Degree jobs?

Cities near Riverside, CA with the most Data Science Degree job openings:

Data Science Degree jobs near you
Infographic showing various Data Science Degree job openings in Riverside, CA as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, and 4% Contract. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $128,049 per year, or $61.6 per hour.

Senior Data Scientist / ML Analytics / BI & Reporting / SQL / Python / Irvine CA

Motion Recruitment Partners, LLC

Lake Forest, CA • On-site

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 13 days ago

Job description

Our client is a global leader in the iOT space for retail loss prevention, operations management, and analytics, with our headquarters based in South OC, California. They maintain a strong presence across the globe, with offices in the UK, Australia, China, Hong Kong, Germany, France, and Canada
They are urgently seeking a Sr. level Data Scientist & Analytics / ML Engineer with strong SQL, Python, Business Intelligence, Reporting Dashboars and Predictive Models.
This individual will play a key role in advancing the company's analytics capabilities beyond traditional business intelligence by developing predictive models, operational analytics, customer intelligence frameworks, and scalable reporting solutions that support proactive decision-making and measurable business impact.
The ideal candidate combines strong technical and analytical expertise with the ability to understand business operations, communicate insights effectively, and partner cross-functionally to solve complex operational and customer challenges.
Role & Responsibilities
Customer & Operational Analytics:
  • Analyze customer, operational, monitoring, video classification, and theft-related data to identify trends, risks, opportunities, and actionable insights.
  • Develop analytical frameworks to measure customer utilization, operational effectiveness, subscription adoption, and customer value realization.
  • Support proactive customer engagement strategies through data-driven insights and trend analysis.
  • Partner with leadership teams to improve visibility into operational and customer performance metrics.
Predictive Modeling & Data Science:
  • Design, build, and maintain predictive models related to theft trends, customer behavior, operational risks, service utilization, and escalation indicators.
  • Develop forecasting and trend analysis models that support operational planning and customer success initiatives.
  • Apply statistical analysis, machine learning, and advanced analytics techniques where appropriate to improve business outcomes.
  • Continuously evaluate and refine model performance and business relevance.
Business Intelligence & Visualization:
  • Develop dashboards, KPI reporting, and analytics tools using Power BI, Tableau, or similar platforms.
  • Create executive-level reporting and operational scorecards that support strategic decision-making.
  • Automate reporting and improve scalability of analytics and data visualization capabilities.
  • Translate complex analytical findings into clear, business-oriented recommendations.
Cross-Functional Collaboration:
  • Partner closely with Operations, Customer Success, Sales, Product Management, IT, Engineering, and Finance teams to identify business opportunities and analytics priorities.
  • Support initiatives involving AI-driven analytics, workflow automation, and operational optimization.
  • Collaborate with technical teams to improve data quality, accessibility, integration, and governance across systems and platforms.
Must Have Skills:
  • Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Business Analytics, or related field.
  • 4-8 years of experience in data science, predictive analytics, customer analytics, operational analytics, or related analytical roles.
  • Strong experience developing predictive models and performing advanced data analysis in business environments.
  • Advanced proficiency in SQL and experience with Python or similar analytics/programming languages.
  • Experience with Power BI, Tableau, or similar business intelligence and visualization tools.
  • Experience working with large, complex operational and customer datasets.
  • Strong analytical, problem-solving, and critical-thinking capabilities.
  • Excellent communication and presentation skills with the ability to explain technical concepts to business stakeholders.
  • Ability to operate independently and manage multiple priorities in a fast-paced environment.
  • Education And/Or Experience : BSEE, MSEE, BSCS, or MSCS
Nice to have / Preferred Skills:
  • Experience in SaaS, retail technology, video analytics, loss prevention, IoT, subscription-based services, or service-oriented organizations.
  • Familiarity with machine learning, AI-driven analytics, and operational optimization techniques.
  • Experience with cloud-based data platforms such as Azure, AWS, or Google Cloud.
  • Experience supporting executive-level operational reporting and KPI development.
The Offer
  • Attractive total compensation package between 130-160k
  • Comprehensive healthcare benefits including medical, dental, and vision coverage; Life/ADD/LTD insurance; FSA/HSA options
  • 401(k) Plan with employer match
  • Generous paid time off policy
  • Observance of 11 paid company holidays

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