Principal Data Scientist

Principal Data Scientist – Enterprise Risk Analytics

Requisition
Job Category:
Accounting / Finance
Job Level: Manager/Principal
Business Unit:
Gen Counsel, Ethics, Risk & Compliance
Work Type:
Hybrid

Job Location:

Oakland

The Enterprise Risk and Operational Risk Management (EORM) organization is responsible for enabling the business to effectively manage risk in key areas of the enterprise through consistent assessment and risk‑informed decision making. EORM oversees all enterprise and operational risk management related to PG&E’s operations and public safety, evaluating risks and mitigations associated with wildfires, nuclear, dams, natural gas, cyberattacks and natural disasters.

As a Principal Data Scientist on the Enterprise Risk Analytics team within EORM, you will lead the development and implementation of quantitative risk models for identified risks including cybersecurity, physical security, data loss, and IT asset failure. Your work will influence enterprise prioritization, investment decisions, and regulatory filings (e.g., RAMP and GRC). You will continuously evaluate and improve quantitative assessments, refining analytical tools and processes such as data processing scripts, risk algorithms, Python programs, Excel files, and Palantir Foundry code to ensure consistent, valuable risk evaluation across the company.

You will support technical development phases for quantitative risk analytics, including data collection, data engineering, modeling, visualization, and user interface design. You will collaborate with both technical and non‑technical coworkers by advising on relevant data collection, resolving analytical and technical challenges, communicating findings and recommendations, and partnering with teams, clients, and senior leadership to ensure continuous improvement. You will review and validate existing methods, assumptions, algorithms, and models, working toward advancing risk analytics at PG&E.

This position is hybrid, working from a remote office and the Oakland General Office at least once per week and based on business needs.

Bay Area Minimum: $159,000
Bay Area Maximum: $236,500

• Apply mathematical, probabilistic, and statistical techniques to quantify risk likelihood and impact, transitioning assessments from subjective ratings to monetized, objective values.
• Collect, clean, and transform data from a variety of internal sources to enable high‑impact analytics; research and implement quantitative methods and machine learning models; lead estimation of mitigation effectiveness and benefit‑cost ratios.
• Partner with subject matter experts, risk managers, and risk owners to integrate quantitative risk assessment into core business and operational processes.
• Mentor and guide junior staff and risk analysts, standardizing processes and tools; collaborate with analytics platform owners to prioritize scalable risk and mitigation modeling capabilities.
• Prepare and deliver clear documentation and presentations on data sources, methodologies, analyses, results, and validations; produce model documentation, whitepapers, formal reports, and expert testimony as required.

Minimum

• Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics, Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.
• 8 years in data science (or 2 years if possessing a Doctoral Degree).

Desired

• Doctoral Degree or higher in a relevant field.
• Relevant industry experience (electric or gas utility, cybersecurity, analytical consulting, etc.) for 8 years.
• Experience quantifying cybersecurity risk using the FAIR framework (certification preferred).
• Experience in quantitative risk analysis or Probabilistic Risk Assessment.
• Strong understanding of mathematical, probabilistic, and statistical foundations underpinning data science and risk modeling.
• Proven proficiency in Monte Carlo simulation methods, Bayesian inference, and application of data science and operations research methodologies.
• Expertise in advanced programming, especially Python, and proficiency with Git in a team environment.
• Excellent analytical,…