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Phd Data Scientist Jobs in Riverside, CA (NOW HIRING)

Prodapt

Senior Data Scientist

Irvine, CA · On-site

We are seeking a highly skilled Data Scientist with expertise in demand forecasting, supply chain ... Advanced degree (MS or PhD) in Operations Research, Applied Mathematics, Computer Science, or ...

Edwards Lifesciences

Staff Data Scientist

Irvine, CA · On-site

$126 - $178/hr

As a Staff Data Scientist, you design the modeling and evaluation approaches that others build on ... or- PhD plus 2 years. Relocation is not provided for this role. Only candidates within a 50-mile ...

BALT Group

Clinical Scientist

Irvine, CA · On-site

Critically analyzes data and prepares presentations for internal and external groups including but ... Qualification Requirements PhD, or Pharm.D preferred, other advanced Medical or Life Sciences ...

BALT Group

Clinical Scientist

Irvine, CA · On-site

Identifies appropriate sources of relevant data, interprets, evaluates and incorporates information ... Qualification Requirements • PhD, or Pharm.D preferred, other advanced Medical or Life Sciences ...

TAE

Scientist - Applied Laser Physics

Foothill Ranch, CA · On-site

$125K - $140K/yr

Ensure data integrity, calibration procedures, and documentation are maintained to high standards ... PhD in Physics, Applied Physics, or Optical Engineering * Preferred: Specialization in laser ...

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All JobsPhd Data Scientist JobsPhd Data Scientist Jobs in Riverside, CA

Phd Data Scientist information

See Riverside, CA salary details

$48K

$172.2K

$254K

How much do phd data scientist jobs pay per year?

As of Aug 21, 2026, the average yearly pay for phd data scientist in Riverside, CA is $172,158.00, according to ZipRecruiter salary data. Most workers in this role earn between $139,300.00 and $177,400.00 per year, depending on experience, location, and employer.

What is a PhD data scientist?

PhD Data Scientists are professionals who have earned a doctoral degree (PhD) in a relevant field, such as computer science, statistics, mathematics, or engineering, and work in roles focused on analyzing and interpreting complex data. They leverage advanced research skills, deep theoretical knowledge, and expertise in data modeling to solve challenging problems, build predictive models, and derive actionable insights for organizations. PhD Data Scientists often contribute to cutting-edge projects, publish research, and help bridge the gap between academic research and practical, real-world applications.

What are the key skills and qualifications needed to thrive as a PhD data scientist?

To thrive as a PhD Data Scientist, you need advanced expertise in statistics, machine learning, and data analysis, typically backed by a PhD in a quantitative field. Proficiency with programming languages like Python or R, experience with big data tools (e.g., Hadoop, Spark), and familiarity with cloud platforms and version control systems are commonly required. Strong problem-solving skills, communication abilities, and the capacity to explain complex concepts to non-technical stakeholders are crucial soft skills. These skills and qualities are essential for extracting actionable insights from complex datasets and driving data-informed decision-making in organizations.

What are some common challenges PhD data scientists face when transitioning from academia to industry roles?

PhD Data Scientists often encounter challenges when moving from academia to industry, such as adapting to faster project timelines, prioritizing business impact over exploratory research, and communicating complex findings to non-technical stakeholders. In industry, there is a greater emphasis on collaborative teamwork and delivering actionable insights that align with organizational goals. Building skills in agile development, stakeholder engagement, and product-focused thinking can help smooth the transition and ensure success in a corporate environment.

What is the difference between Phd Data Scientist vs Data Analyst?

AspectPhd Data ScientistData Analyst
Required CredentialsPhD or Master's in Data Science, Statistics, or related fieldBachelor's or Master's in related field, often with certifications
Work EnvironmentResearch-focused, complex modeling, advanced analyticsBusiness reporting, data visualization, basic analysis
Employer & Industry UsageTech, academia, research institutions, large corporationsBusiness, marketing, finance, healthcare

Phd Data Scientists typically have advanced degrees and focus on complex modeling and research, while Data Analysts handle more straightforward data reporting and visualization tasks. Both roles are essential in data-driven organizations but differ in scope and expertise.

What are popular job titles related to Phd Data Scientist jobs in Riverside, CA?

For Phd Data Scientist jobs in Riverside, CA, the most frequently searched job titles are:

What job categories do people searching Phd Data Scientist jobs in Riverside, CA look for?

The top searched job categories for Phd Data Scientist jobs in Riverside, CA are:

What cities near Riverside, CA are hiring for Phd Data Scientist jobs?

Cities near Riverside, CA with the most Phd Data Scientist job openings:

Phd Data Scientist jobs near you
Infographic showing various Phd Data Scientist job openings in Riverside, CA as of August 2026, with employment types broken down into 94% Full Time, and 6% Contract. Highlights an 94% In-person, and 6% Remote job distribution, with an average salary of $172,158 per year, or $82.8 per hour.

Senior Data Scientist

Prodapt

Irvine, CA • On-site

Other

Posted 26 days ago

Job description

We are seeking a highly skilled Data Scientist with expertise in demand forecasting, supply chain optimization, and retail inventory management. In this role, you will develop, retrain, and validate demand forecasting models tailored to multiple regional markets, while collaborating closely with the optimization team to enhance inventory allocation and replenishment strategies. You will work with large-scale retail datasets, deploy models using AWS SageMaker, and operate within a federated data architecture to ensure accurate, scalable forecasting solutions.

Key Responsibilities:

  • Develop, retrain, and adapt demand forecasting models (ARIMA, Prophet, neural networks) to reflect regional seasonality, buying patterns, and lead times.
  • Calibrate and validate forecast accuracy using federated regional data to meet go-live thresholds before market activations.
  • Collaborate with the supply chain optimization team to provide inputs for inventory allocation and replenishment engines.
  • Translate complex business constraints into mathematical optimization models using linear programming and constraint satisfaction techniques.
  • Design and implement optimization solutions for retail inventory allocation and replenishment using Python libraries (PuLP, OR-Tools) and solvers (Gurobi, CPLEX).
  • Deploy and maintain forecasting and optimization models on AWS SageMaker, integrating with Lambda and other AWS services for scalable workflows.
  • Work independently with architectural guidance from lead scientists, and mentor junior applied scientists as needed.
  • Communicate model insights and business impact effectively to cross-functional teams.

Required Qualifications:

  • Strong experience in time series forecasting methods such as ARIMA, Prophet, and neural forecasting models (LSTM, RNN).
  • Proficiency in mathematical optimization techniques including linear programming, constraint satisfaction, and multi-objective optimization.
  • Hands-on experience with Python and relevant libraries: pandas, numpy, scikit-learn, statsmodels, PuLP, OR-Tools.
  • Familiarity with optimization solvers such as Gurobi or CPLEX.
  • Experience working with large-scale retail datasets and federated data architectures.
  • Expertise in retail demand planning, demand sensing, and supply chain or inventory management.
  • Proficient in AWS ML stack, especially SageMaker for model training and deployment, and Lambda for serverless integration.
  • Strong SQL skills for data extraction and manipulation.
  • Ability to translate business requirements into mathematical and computational models.
  • Excellent problem-solving skills and ability to work independently.
  • Experience mentoring or leading applied scientists is a plus.
  • Advanced degree (MS or PhD) in Operations Research, Applied Mathematics, Computer Science, or related field.

Preferred Qualifications:

  • Experience with store allocation and replenishment systems.
  • Familiarity with agentic AI frameworks or advanced AI-driven decision-making systems.
  • Knowledge of CI/CD pipelines for ML model deployment.
  • Multi-market or international retail exposure.

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