ZipRecruiter

Log In

1

Predictive Modeling Analyst Jobs in California (NOW HIRING)

Esri

Product Analyst

Redlands, CA · On-site

$70K - $110K/yr

Apply advanced analytics and predictive modeling to product sales, usage, and customer data to inform strategy, pricing, and growth * Create executive ready reports, and deep analyses that clearly ...

Jobtailor

People Operations Manager

Los Angeles, CA · On-site

Build and scale workforce analytics capabilities supporting business decision-making * Develop predictive models focused on attrition, talent attraction, internal talent mobility, workforce planning ...

New

Vscore Inc.

Data Analyst

Sacramento, CA · On-site

Familiarity with Python, R, or other statistical analysis tools for predictive modeling and trend forecasting. * Understanding of database management systems (DBMS) such as Oracle, MySQL, PostgreSQL ...

Golden State

Data Scientist

San Francisco, CA · Remote

$165K/yr

Build features for predictive models * Partner with data analysts to integrate the results into decision making * Analyze large data sets to discover trends and patterns * Maintain a pulse on the ...

ASK Consulting

Data Scientist III

Mountain View, CA · On-site

The role partners closely with marketing stakeholders to drive data-informed decision-making through statistical analysis, experimentation, predictive modeling, reporting, and performance measurement.

AEG

Data Scientist

San Francisco, CA · On-site

$165K/yr

Build features for predictive models * Partner with data analysts to integrate the results into decision making * Analyze large data sets to discover trends and patterns * Maintain a pulse on the ...

previous pagenext page

Showing results 21-40

All JobsPredictive Modeling Analyst JobsPredictive Modeling Analyst Jobs in California

Predictive Modeling Analyst information

See California salary details

$32.3K

$97.4K

$183.1K

How much do predictive modeling analyst jobs pay per year?

As of Sep 13, 2026, the average yearly pay for predictive modeling analyst in California is $97,365.00, according to ZipRecruiter salary data. Most workers in this role earn between $61,201.00 and $123,382.00 per year, depending on experience, location, and employer.

What does a predictive modeling analyst do?

A Predictive Modeling Analyst uses statistical techniques and data analysis tools to forecast future outcomes based on historical data. They build mathematical models to identify patterns and trends, helping organizations make data-driven decisions in areas like marketing, finance, and operations. Their work often involves cleaning data, selecting relevant variables, and evaluating the accuracy of their models to ensure reliable predictions.

What are the key skills and qualifications needed to thrive as a predictive modeling analyst?

To thrive as a Predictive Modeling Analyst, you need strong statistical analysis, data mining, and mathematical modeling skills, typically supported by a degree in statistics, mathematics, computer science, or a related field. Proficiency with analytical tools and languages such as Python, R, SAS, and familiarity with data visualization software and databases is essential. Critical thinking, problem-solving, and effective communication are key soft skills that help convey complex findings to stakeholders. These skills and qualities are crucial for generating accurate predictions and actionable insights that drive strategic business decisions.

How does a predictive modeling analyst typically collaborate with other departments within an organization?

Predictive Modeling Analysts often work closely with departments such as Marketing, Finance, Operations, and IT to understand business needs and deliver actionable insights. They translate complex statistical findings into clear recommendations, ensuring stakeholders from non-technical backgrounds can make informed decisions. Regular meetings, cross-functional project teams, and data-sharing platforms are common collaboration tools, making strong communication and teamwork skills essential. This collaborative approach not only improves model effectiveness but also helps align predictive analytics with overall business strategies.

How to become a predictive modeling analyst?

To become a predictive modeling analyst, typically a bachelor's degree in fields like statistics, mathematics, or data science is required. Developing skills in programming languages such as Python or R, understanding machine learning algorithms, and gaining experience with data analysis tools are essential. Earning certifications in data science or analytics can also enhance job prospects.
Predictive Modeling Analyst jobs near you
Infographic showing various Predictive Modeling Analyst job openings in California as of August 2026, with employment types broken down into 81% Full Time, 15% Part Time, and 4% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $97,365 per year, or $46.8 per hour.

Data scientist workforce, product, Operations analytics *** Direct end client ***

San Diego, CA • On-site

Projas Technologies, LLC
1 - 10 employees
Learn more about Projas Technologies, LLCexternal

Other

Posted 12 days ago

Job description

Data scientist workforce, product & Operations Analytics

Position Overview

In this role, you will support the operations team’s strategy determining the optimal mix of internal, third-party, domestic, and international workforce resources supporting our financial and software platforms.

Your primary responsibility will be to go beyond static dashboards to build predictive and explanatory models that project the operational and financial impact of these workforce shifts. You will evaluate critical trade-offs between labor cost premiums and customer experience outcomes (such as handle times, transfer rates, and resolution rates) to drive rapid, data-backed strategic decisions.

Key Responsibilities

  • Perform Advanced Business Analysis: Formulate data-backed strategies using statistical analysis, predictive modeling, and data mining to drive step-function growth and increase customer benefits.
  • Evaluate Strategic Workforce & Labor Models: Analyze trade-offs between internal vs. external and credentialed vs. non-credentialed workforce segments. Determine if paying a premium for specific models (such as onshore resources) is net-neutral or positive by evaluating impacts on handle times, resolve rates, and contact volume.
  • Consolidate and Mix-Adjust Metrics: Combine isolated performance indicators (including customer satisfaction, transfer rates, average handle time, and financial performance) into a single, holistic topology view. Apply mix-adjustments to control for contact complexity, volume, and tenure to ensure fair comparisons across distinct labor pools.
  • Build Data Pipelines and Staging Tables: Access the corporate data lake to extract and transform raw data into custom staging tables within an orchestration framework ensuring other teams can easily access consolidated topology data.
  • Analyze Multimodal Data: Merge highly structured, tabular databases with unstructured datasets (such as customer chat transcripts, calls, and written anecdotes) to provide a complete picture of customer pain points.
  • Collaborate and Align Stakeholders: Work directly with cross-functional working teams—including finance, operations, and external data science groups. Align on data inputs and sources up front to ensure business reviews focus on debating outputs and strategic actions rather than arguing over data validity.
  • Support Agile, Ad Hoc Analysis: Utilize modern AI integrations (such as Claude and GitHub workflows) alongside traditional platforms to rapidly generate insights and solve immediate operational questions.

Required Skills & Experience

  • Experience: 5+ years of experience in data science, workforce analytics, or product analytics (preferably in a fintech or financial services environment).
  • Education: BS or MS degree in Statistics, Mathematics, Computer Science, or a related quantitative field.
  • Statistical Strategy & Modeling: Strong background in statistical modeling, hypothesis generation, and experimental design. Demonstrated capability to operate independently to solve open-ended strategic problems rather than simply executing tasks.
  • Causal Inference: Hands-on experience with advanced causal inference techniques, specifically propensity score matching, difference-in-differences (DiD), and synthetic control methods.
  • Programming & Tools:
    • Advanced SQL skills for data extraction, manipulation, and pipeline creation.
    • Strong Python proficiency (including NumPy, Pandas, Scikit-learn, and related libraries).
    • Ability to use Generative AI and modern developer tools (e.g., Claude, GitHub integrations) to accelerate analytical workflows.
  • Data Visualization: Experience with scalable BI and reporting platforms, with a strong preference for Qlik Sense or Tableau.
  • Communication: Outstanding communication skills. Must be able to walk non-technical working-level teams (finance, business partners) through complex data logic to build consensus and drive swift decisions.

Preferred Qualifications

  • Call Center Domain Expertise: Prior experience analyzing contact center or customer support metrics (e.g., Average Handle Time/AHT, resolution rates, transfer rates, conversion) is highly desirable.

Workforce Analytics, Operations Research, Causal Inference, Propensity Score Matching, Difference-in-Differences, Statistical Modeling, Data Lake, Superglue, Qlik Sense, Tableau, Multimodal Data, Predictive Modeling, FinTech, Contact Center Metrics, AHT, Workforce Topology, Staging Tables

Data Science, Product Analytics, Business Analytics, SQL, Advanced SQL, Python, Pandas, NumPy, Scikit-learn, SciPy, Statsmodels, Predictive Modeling, Statistical Analysis, Machine Learning, Data Mining, Customer Segmentation, Experimentation, A/B Testing, Causal Inference, Propensity Score Matching, PSM, Difference-in-Differences
Senior Data Scientist, Data Scientist, Staff Data Scientist, Lead Data Scientist, Principal Data Scientist, Product Data Scientist, Product Analytics Scientist, Senior Product Analyst, Product Analytics Manager, Senior Analytics Consultant, Analytics Consultant, Business Data Analyst, Senior Business Data Analyst, Quantitative Analyst, Senior Quantitative Analyst, Decision Scientist, Senior Decision Scientist, Applied Scientist, Senior Applied Scientist, Machine Learning Scientist, Machine Learning Engineer, Customer Analytics Manager, Growth Analytics Manager, Product Analytics Manager, Data Analytics Manager, Insights Manager, Business Intelligence Analyst, Senior Business Intelligence Analyst, BI Analyst, BI Developer, Analytics Engineer, Senior Analytics Engineer, Experimentation Scientist, Experimentation Analyst, Causal Inference Scientist, Econometrician, Marketing Scientist, Consumer Insights Analyst, Behavioral Scientist, Predictive Analytics Specialist, Statistical Analyst, Research Scientist, AI Data Scientist, Generative AI Analyst, Advanced Analytics Consultant, Data Strategy Consultant, Decision Analytics Consultant, Quantitative Research Analyst, Customer Insights Analyst, Growth Scientist, Revenue Analytics Manager, Digital Analytics Manager, FinTech Data Scientist, FinTech Analytics Manager, Risk Analytics Scientist, Data Science Consultant, Principal Analytics Consultant, Product Insights Analyst, Data Modeling Specialist, Forecasting Analyst, Measurement Scientist, Business Analytics Manager, Senior Business Analytics Manager

Apply