Data Scientist Jobs in California
Data Scientist jobs in California are among the most active in the country, with demand concentrated in technology, healthcare, financial services, and entertainment analytics at every level from entry-level analyst through principal and staff data scientist. The San Francisco Bay Area, Los Angeles, and San Diego are the largest hiring markets, home to anchoring employers like Google, Kaiser Permanente, and Wells Fargo, all of which maintain substantial California data science teams. The most in-demand specializations include machine learning engineering, natural language processing, and applied analytics for product and clinical decision-making. Find a role that fits below and apply directly.
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Showing 5 of 834+ Data Scientist jobs
Principal Data Scientist
Location: Dublin, CA (Hybrid 1-2 days in a week)
Job Type: Contract
LOCAL CANDIDATES ONLY
The role is Hybrid. 1-2 days a week in Dublin. There may be times when we need to travel to other locations such as Oakland, Concord, or field sites around the service area. Client laptop will be provided. PPE: Client will provide, if needed, hardhat, vest, safety glasses, etc. With prior Manager approval, may submit expense, at a set amount for internet/phone reimbursements.
Position Summary:
We are seeking a highly analytical and mission-driven Data Scientist to support the development of a quantitative risk analysis and predictive analytics capability for Transmission Right of Way (ROW) Risk Reduction Strategy. This role will help design and operationalize data-driven methods to quantify risk, prioritize encroachments, and predict the likelihood of safety and reliability events associated with transmission right of way encroachments. The successful candidate will partner with cross-functional teams across electric operations, asset management, vegetation management, engineering, risk, compliance, GIS, inspection, and program management to translate field, asset, and operational data into actionable insights. The Data Scientist will build models that enable proactive decision-making by identifying where encroachments pose the greatest potential threat to public safety, worker safety, grid reliability, asset integrity, and wildfire risk. This role is ideal for someone who combines deep technical expertise in statistical modeling and machine learning with the ability to work in complex operational environments and communicate insights to business and executive stakeholders.
Key Responsibilities
Quantitative Risk Modeling
- Develop quantitative risk frameworks to assess the risk posed by encroachments within or adjacent to transmission rights of way.
- Define risk equations, scoring methodologies, and analytical models that estimate both:
- Likelihood of an event occurring (e.g., safety incident, reliability event, asset damage, access impairment, wildfire ignition, clearance violation, line contact, third-party interference), and
- Consequence / impact of that event.
- Incorporate multiple risk dimensions into a unified analytical framework, including:
- Public and employee safety
- Electric reliability / outage exposure
- Wildfire and ignition risk
- Regulatory and compliance exposure
- Asset damage and access limitations
- Financial and operational impact
Predictive Analytics & Machine Learning
- Build predictive models to estimate the likelihood of future safety or reliability events resulting from existing or emerging encroachments in transmission rights of way.
- Apply statistical and machine learning techniques such as:
- Logistic regression
- Survival analysis / time-to-event modeling
- Random forests / gradient boosting
- Bayesian methods
- Scenario modeling and simulation
- Geospatial and spatiotemporal modeling
- Identify leading indicators and risk drivers that increase the probability of an event, such as:
- Proximity to energized assets
- Encroachment type and severity
- Clearance deficits
- Structure condition / asset age
- Land use and development patterns
- Historical incident patterns
- Inspection findings
- Environmental and weather conditions
- Access constraints
- High Fire Threat District (HFTD) or other high-risk locations
Data Integration & Analytical Pipeline Development
- Aggregate, clean, and structure data from multiple enterprise and operational systems, including GIS, asset management, inspections, outage history, incident data, vegetation data, work management, and field observations.
- Develop repeatable analytical pipelines to support risk scoring, trend analysis, forecasting, and prioritization.
- Assess data quality, completeness, and lineage; identify data gaps and recommend improvements to enable stronger analytics.
- Partner with IT, data engineering, GIS, and business teams to improve data architecture and enable scalable model deployment.
Decision Support & Program Prioritization
- Translate model outputs into practical prioritization tools that support program strategy, annual planning, and execution.
- Develop dashboards, visualizations, and decision-support tools to help the business:
- Rank encroachments by risk
- Identify high-priority mitigation opportunities
- Forecast emerging risk hotspots
- Evaluate tradeoffs across mitigation options
- Support resource allocation and investment decisions
- Support the development of business cases and analytical narratives for leadership, regulators, and governance forums.
Monitoring, Validation & Continuous Improvement
- Establish model validation, calibration, and performance monitoring processes to ensure analytics remain accurate, explainable, and fit for purpose.
- Track model precision, recall, false positives/negatives, drift, and operational usefulness over time.
- Conduct sensitivity analyses, scenario testing, and back-testing against historical events.
- Continuously improve methodologies as new data sources, field intelligence, and business requirements emerge.
Cross-Functional Collaboration
- Partner closely with subject matter experts in transmission operations, inspection, engineering, wildfire mitigation, risk management, land/ROW, and compliance to ensure models reflect real-world operating conditions.
- Facilitate discussions to define risk taxonomy, modeling assumptions, thresholds, and action triggers.
- Communicate technical findings clearly to both technical and non-technical stakeholders, including senior leadership.
Required Qualifications
- Bachelor’s degree in Data Science, Statistics, Applied Mathematics, Engineering, Computer Science, Operations Research, Economics, or a related quantitative field.
- 5+ years of experience in data science, predictive analytics, quantitative risk analysis, or statistical modeling.
- Experience building predictive models using Python, R, SQL, or similar tools.
- Strong knowledge of:
- Statistical inference
- Machine learning
- Risk modeling
- Forecasting
- Feature engineering
- Data wrangling and data quality management
- Experience working with large, complex, and imperfect datasets from multiple business systems.
- Ability to explain technical results to operational and executive audiences in a clear, concise, and decision-oriented manner.
- Demonstrated ability to turn ambiguous business problems into structured analytical approaches.
Preferred Qualifications
- Master’s or PhD in a quantitative discipline.
- Experience in electric utility, transmission operations, wildfire risk, asset risk management, infrastructure risk, public safety risk, or reliability analytics.
- Experience with geospatial analytics, including GIS-based risk modeling.
- Familiarity with transmission asset data, ROW management, encroachment data, inspection data, outage/event history, or utility asset health data.
- Experience in regulated industries where transparency, traceability, and model explainability are essential.
- Knowledge of safety and reliability risk concepts in utility operations.
- Experience developing dashboards or decision-support tools using Power BI, Tableau, or similar platforms.
- Familiarity with cloud analytics environments and productionizing models for business use.
Technical Skills
- Programming: Python, R, SQL
- Analytics: Statistical modeling, machine learning, forecasting, simulation, optimization
- Data tools: Data wrangling, ETL concepts, data quality assessment
- Visualization: Power BI, Tableau, matplotlib, seaborn, or similar
- Geospatial: ArcGIS, QGIS, GeoPandas, spatial analysis techniques
Modeling concepts:
- Classification and probability prediction
- Risk scoring frameworks
- Time-to-event / hazard models
- Explainable AI / interpretable models
- Scenario analysis and Monte Carlo methods
Key Competencies
- Strong problem-solving and structured thinking
- Ability to work across technical and operational disciplines
- High attention to detail and analytical rigor
- Strong business acumen and decision orientation
- Comfort working in evolving, ambiguous problem spaces
- Ability to balance model sophistication with usability and explainability
- Excellent written and verbal communication skills.
Applicant Notices & Disclaimers
For information on benefits, equal opportunity employment, and location-specific applicant notices, click here. At SPECTRAFORCE, we are committed to maintaining a workplace that ensures fair compensation and wage transparency in adherence with all applicable state and local laws. Ball Park range is $100-$150/hr w2.
Principal Data Scientist
Location: Dublin, CA (Hybrid 1-2 days in a week)
Job Type: Contract
LOCAL CANDIDATES ONLY
The role is Hybrid. 1-2 days a week in Dublin. There may be times when we need to travel to other locations such as Oakland, Concord, or field sites around the service area. Client laptop will be provided. PPE: Client will provide, if needed, hardhat, vest, safety glasses, etc. With prior Manager approval, may submit expense, at a set amount for internet/phone reimbursements.
Position Summary:
We are seeking a highly analytical and mission-driven Data Scientist to support the development of a quantitative risk analysis and predictive analytics capability for Transmission Right of Way (ROW) Risk Reduction Strategy. This role will help design and operationalize data-driven methods to quantify risk, prioritize encroachments, and predict the likelihood of safety and reliability events associated with transmission right of way encroachments. The successful candidate will partner with cross-functional teams across electric operations, asset management, vegetation management, engineering, risk, compliance, GIS, inspection, and program management to translate field, asset, and operational data into actionable insights. The Data Scientist will build models that enable proactive decision-making by identifying where encroachments pose the greatest potential threat to public safety, worker safety, grid reliability, asset integrity, and wildfire risk. This role is ideal for someone who combines deep technical expertise in statistical modeling and machine learning with the ability to work in complex operational environments and communicate insights to business and executive stakeholders.
Key Responsibilities
Quantitative Risk Modeling
- Develop quantitative risk frameworks to assess the risk posed by encroachments within or adjacent to transmission rights of way.
- Define risk equations, scoring methodologies, and analytical models that estimate both:
- Likelihood of an event occurring (e.g., safety incident, reliability event, asset damage, access impairment, wildfire ignition, clearance violation, line contact, third-party interference), and
- Consequence / impact of that event.
- Incorporate multiple risk dimensions into a unified analytical framework, including:
- Public and employee safety
- Electric reliability / outage exposure
- Wildfire and ignition risk
- Regulatory and compliance exposure
- Asset damage and access limitations
- Financial and operational impact
Predictive Analytics & Machine Learning
- Build predictive models to estimate the likelihood of future safety or reliability events resulting from existing or emerging encroachments in transmission rights of way.
- Apply statistical and machine learning techniques such as:
- Logistic regression
- Survival analysis / time-to-event modeling
- Random forests / gradient boosting
- Bayesian methods
- Scenario modeling and simulation
- Geospatial and spatiotemporal modeling
- Identify leading indicators and risk drivers that increase the probability of an event, such as:
- Proximity to energized assets
- Encroachment type and severity
- Clearance deficits
- Structure condition / asset age
- Land use and development patterns
- Historical incident patterns
- Inspection findings
- Environmental and weather conditions
- Access constraints
- High Fire Threat District (HFTD) or other high-risk locations
Data Integration & Analytical Pipeline Development
- Aggregate, clean, and structure data from multiple enterprise and operational systems, including GIS, asset management, inspections, outage history, incident data, vegetation data, work management, and field observations.
- Develop repeatable analytical pipelines to support risk scoring, trend analysis, forecasting, and prioritization.
- Assess data quality, completeness, and lineage; identify data gaps and recommend improvements to enable stronger analytics.
- Partner with IT, data engineering, GIS, and business teams to improve data architecture and enable scalable model deployment.
Decision Support & Program Prioritization
- Translate model outputs into practical prioritization tools that support program strategy, annual planning, and execution.
- Develop dashboards, visualizations, and decision-support tools to help the business:
- Rank encroachments by risk
- Identify high-priority mitigation opportunities
- Forecast emerging risk hotspots
- Evaluate tradeoffs across mitigation options
- Support resource allocation and investment decisions
- Support the development of business cases and analytical narratives for leadership, regulators, and governance forums.
Monitoring, Validation & Continuous Improvement
- Establish model validation, calibration, and performance monitoring processes to ensure analytics remain accurate, explainable, and fit for purpose.
- Track model precision, recall, false positives/negatives, drift, and operational usefulness over time.
- Conduct sensitivity analyses, scenario testing, and back-testing against historical events.
- Continuously improve methodologies as new data sources, field intelligence, and business requirements emerge.
Cross-Functional Collaboration
- Partner closely with subject matter experts in transmission operations, inspection, engineering, wildfire mitigation, risk management, land/ROW, and compliance to ensure models reflect real-world operating conditions.
- Facilitate discussions to define risk taxonomy, modeling assumptions, thresholds, and action triggers.
- Communicate technical findings clearly to both technical and non-technical stakeholders, including senior leadership.
Required Qualifications
- Bachelor’s degree in Data Science, Statistics, Applied Mathematics, Engineering, Computer Science, Operations Research, Economics, or a related quantitative field.
- 5+ years of experience in data science, predictive analytics, quantitative risk analysis, or statistical modeling.
- Experience building predictive models using Python, R, SQL, or similar tools.
- Strong knowledge of:
- Statistical inference
- Machine learning
- Risk modeling
- Forecasting
- Feature engineering
- Data wrangling and data quality management
- Experience working with large, complex, and imperfect datasets from multiple business systems.
- Ability to explain technical results to operational and executive audiences in a clear, concise, and decision-oriented manner.
- Demonstrated ability to turn ambiguous business problems into structured analytical approaches.
Preferred Qualifications
- Master’s or PhD in a quantitative discipline.
- Experience in electric utility, transmission operations, wildfire risk, asset risk management, infrastructure risk, public safety risk, or reliability analytics.
- Experience with geospatial analytics, including GIS-based risk modeling.
- Familiarity with transmission asset data, ROW management, encroachment data, inspection data, outage/event history, or utility asset health data.
- Experience in regulated industries where transparency, traceability, and model explainability are essential.
- Knowledge of safety and reliability risk concepts in utility operations.
- Experience developing dashboards or decision-support tools using Power BI, Tableau, or similar platforms.
- Familiarity with cloud analytics environments and productionizing models for business use.
Technical Skills
- Programming: Python, R, SQL
- Analytics: Statistical modeling, machine learning, forecasting, simulation, optimization
- Data tools: Data wrangling, ETL concepts, data quality assessment
- Visualization: Power BI, Tableau, matplotlib, seaborn, or similar
- Geospatial: ArcGIS, QGIS, GeoPandas, spatial analysis techniques
Modeling concepts:
- Classification and probability prediction
- Risk scoring frameworks
- Time-to-event / hazard models
- Explainable AI / interpretable models
- Scenario analysis and Monte Carlo methods
Key Competencies
- Strong problem-solving and structured thinking
- Ability to work across technical and operational disciplines
- High attention to detail and analytical rigor
- Strong business acumen and decision orientation
- Comfort working in evolving, ambiguous problem spaces
- Ability to balance model sophistication with usability and explainability
- Excellent written and verbal communication skills.
Applicant Notices & Disclaimers
For information on benefits, equal opportunity employment, and location-specific applicant notices, click here. At SPECTRAFORCE, we are committed to maintaining a workplace that ensures fair compensation and wage transparency in adherence with all applicable state and local laws. Ball Park range is $100-$150/hr w2.
See All 834+ Data Scientist Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
Find Data Scientist JobsData Scientist Jobs by City in California
Where California roles are concentrated, by current openings.
Data Scientist Job Market in California
A snapshot from current California openings, updated as new roles post.
Who's Hiring
- TikTok103
- ByteDance44
- Amazon43
- Apple35
- Intuit29
Top Industries Hiring
- Technology & Software387
- Electronics & Hardware65
- Artificial Intelligence53
- Biotechnology & Pharmaceuticals51
- Science & Research44
What California Employers Look For
The qualifications that appear most often in data scientist jobs across California.
- Bachelor's or master's degree in statistics, computer science, mathematics, or a related quantitative field
- Proficiency in Python and SQL for data manipulation, modeling, and pipeline development
- Hands-on experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch
- Ability to communicate findings to non-technical stakeholders through clear written and visual reports
- Experience working with large datasets using cloud platforms such as AWS, Google Cloud, or Azure
- Familiarity with experiment design, A/B testing, and statistical inference in a product or research context
Data Scientist Jobs in California: Frequently Asked Questions
How do you become a data scientist in California?
Data science has no state-issued license in California, so the path runs through education and demonstrated skills. Most California employers expect at least a bachelor's degree in a quantitative field, with a master's degree preferred at mid-level and above. Building a portfolio of projects, contributing to open-source work, and completing graduate-level coursework through California's UC or CSU system are the most direct routes into the field.
How much do data scientists make in California?
Data scientists in California earn a median of about $141,590 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $77,480 for the lowest 10% to over $224,920 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire data scientists in California?
Employers hiring data scientists in California right now include TikTok, ByteDance, and Amazon, based on current listings on Migrate Mate as of June 2026. California's concentration of technology headquarters, large health systems, and major financial institutions means openings appear across industries, not just in software companies.
Which California cities have the most data scientist jobs?
San Francisco, San Jose, and Mountain View have the most data scientist openings in California. The San Francisco Bay Area leads because of its dense concentration of technology headquarters and venture-backed startups, while Los Angeles draws from entertainment analytics, healthcare, and aerospace, and San Diego's biotech and defense sectors drive steady demand there.
Are there remote data scientist jobs in California?
Yes, and more than most fields. About 26% of data scientist openings tied to California are remote or hybrid as of June 2026, reflecting how central this work is to software and analytics products built entirely in distributed environments. Roles focused on modeling and reporting tend to be the most remote-friendly, while positions requiring close collaboration with engineering teams or clinical operations are more likely to require on-site presence.
How can I get hired as a data scientist in California with little or no experience?
The most realistic entry path is applying for analyst or junior data scientist roles at large California technology, health, or financial services employers that run structured new-grad programs. Companies like Kaiser Permanente, Salesforce, and major UC medical centers offer rotational or associate-level positions designed for recent graduates. Building a portfolio of end-to-end projects in Python and SQL, completing a master's program at a UC or CSU campus, or moving laterally from a data analyst or business intelligence role are the approaches that most consistently open doors in California.
Where can I find and apply to data scientist jobs in California?
You can find and apply to data scientist jobs in California on Migrate Mate, which lists current California openings updated regularly. Search the listings for roles that match your experience level and location preference, then apply directly to the ones that fit.
See All 834+ Data Scientist Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
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