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Data Science Jobs in Riverside, CA (NOW HIRING)

omega solutions, Inc.

Sr. Data Scientist

Santa Ana, CA · On-site

Sr. Data Scientist Santa Ana, CA 6 months : What is the specific title of the position? Senior Data Scientist What Project/Projects will the candidate be working on while on assignment? 1) Field Team ...

Fujifilm

Director Data AI & Analytics

Irvine, CA · Hybrid

Lead data science programs for manufacturing quality, supply chain, and R&D. * Own bioinformatics analytics and pipelines (e.g., NGS/omics workflows, imaging pipelines, assay analytics) and ...

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 JobsData Science Jobs in Riverside, CA

Data Science information

See Riverside, CA salary details

$39.1K

$128K

$205K

How much do data science jobs pay per year?

As of Sep 4, 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.

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 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 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.

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 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.

Is a data scientist in high demand?

Data scientists are in high demand across many industries due to the increasing reliance on data-driven decision making. The role requires skills in programming, statistics, and machine learning, and job growth is expected to continue as organizations expand their data capabilities.

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.
More about Data Science jobs

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:

Data Science jobs near you
Infographic showing various Data Science job openings in Riverside, CA as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 16% Part Time, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $128,049 per year, or $61.6 per hour.

Data Scientist, AI Data Foundations

NextDeavor Inc.

Irvine, CA • Remote

$114K - $175K/yr

Contractor

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Data Scientist, AI Data Foundations
Full-time
Remote
Exclusive confidential search — details shared with qualified applicants.
 
Become a Key Player as a Data Scientist, AI Data Foundations

You will design and build the curated data structures that AI and ML applications consume, enabling higher-quality model training and inference. You will partner with model builders, product, risk, and growth stakeholders to surface actionable insights and ship production-ready vector, feature, and graph data assets. This is a Remote role.

Here's How You'll Make an Impact on the Team
  • Build and maintain vector stores for RAG, including embedding pipelines, chunking strategies, indexing, and refresh patterns.
  • Own the feature store: design, build, and operate feature definitions, freshness SLAs, lineage, and point-in-time correctness for offline/online use.
  • Design and implement graph data structures to model relationships across applicants, applications, products, lenders, decisions, and outcomes.
  • Lead data discovery: profile lending, deposit, and behavioral datasets to identify trends, segments, anomalies, and model drivers; produce actionable hypotheses for stakeholders.
  • Engineer curated, AI-ready datasets with appropriate quality checks, documentation, and governance for downstream model builders and analysts.
  • Define and run evaluation frameworks for RAG retrieval quality, feature drift, embedding quality, and graph completeness; iterate on metrics.
  • Partner closely with ML engineers and applied scientists to ensure data assets accelerate model development and serving workflows.
  • Champion responsible data use by collaborating with governance, security, and compliance teams to ensure data classification, consent, and regulatory boundaries are respected.
  • Communicate findings via write-ups, notebooks, dashboards, and short presentations for technical and non-technical audiences.
Here's What You'll Need to Be Successful in This Role
  • 4–7 years of experience in data science, ML engineering, or applied data roles, with significant time building data assets consumed by models or applications.
  • Hands-on experience designing and operating vector stores for RAG or semantic search (embedding generation, chunking, indexing, retrieval evaluation).
  • Experience building or operating a feature store (e.g., Databricks Feature Store, Feast, or custom), including offline training and online serving patterns and point-in-time correctness.
  • Experience modeling and building graph data structures and writing graph queries (Neo4j, TigerGraph, Cosmos DB Gremlin, or similar).
  • Strong proficiency in Python (pandas, NumPy, scikit-learn, PySpark) and SQL; comfortable using Databricks notebooks and jobs.
  • Practical experience with embedding models and LLM tooling (Hugging Face, OpenAI/Azure OpenAI APIs, LangChain or similar) in production or near-production contexts.
  • Demonstrated data discovery skills: profiling messy datasets, surfacing patterns, validating findings statistically, and explaining results clearly.
  • Solid grounding in classical ML concepts (supervised vs. unsupervised learning, train/test discipline, leakage, evaluation metrics).
  • Strong written and verbal communication skills for technical and business audiences.
Here's What Else Might Help You Out
  • Experience in SaaS or FinTech, especially with lending, deposit, credit, fraud, or KYC/AML data.
  • Familiarity with Databricks-native AI/ML tooling: Databricks Vector Search, Databricks Feature Store, MLflow, Unity Catalog.
  • Experience with open-source vector DBs (pgvector, Pinecone, Weaviate, Chroma, FAISS) and strong opinions on trade-offs.
  • Experience with Microsoft Azure data and AI services (Azure OpenAI, Azure AI Search, ADLS Gen2).
  • Experience evaluating RAG systems end-to-end (recall@k, faithfulness, answer quality, hallucination measurement).
  • Exposure to graph algorithms (community detection, link prediction, centrality) applied to business problems.
  • Bachelor's or Master's in CS, Statistics, Mathematics, Engineering, or related quantitative field, or equivalent experience.
Pay Range

$114,000 - $175,000/year

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