Principal Data Scientist

Requisition ID # 169267

Job Category: Accounting / Finance

Job Level: Manager/Principal

Business Unit: Gen Counsel, Ethics, Risk & Compliance

Work Type: Hybrid

Job Location: Oakland

Department Overview

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 organization is charged with overseeing all enterprise and operational risk management related to PG&E’s operations and public safety including evaluating risks and their mitigations associated with wildfires, nuclear, dams, natural gas, cyberattacks and natural disasters.

Position Summary

As a Principal Data Scientist on the Enterprise Risk Analytics team within the EORM organization, you will be responsible for leading the development and implementation of quantitative risk models for identified risks including cybersecurity, physical security, data loss , and IT asset failure—as well as evaluating risk reduction achieved through mitigation and controls. Your work will influence enterprise prioritization, investment decisions, and regulatory filings (such as Risk Assessment and Mitigation Phase (RAMP) and General Rate Case (GRC)).

You will continuously evaluate and improve quantitative assessments of risk and associated mitigations and controls while refining analytical tools and processes—such as data processing scripts, risk algorithms, Python programs, Excel files, and Palantir Foundry code—to ensure consistent and valuable risk evaluation across the company. Responsibilities include designing, developing, and executing scripts, programs, models, algorithms, and processes using structured and unstructured data from various sources and at various scales, with the goal of producing actionable insights for strategy, planning, process improvement, and product enhancement.

Additionally, 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 throughout the development cycle to ensure continuous improvement. Furthermore, you will review and validate existing methods, assumptions, algorithms, and models, working toward the advancement of risk analytics at PG&E.

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

PG&E is providing the salary range that the company in good faith believes it might pay for this position at the time of the job posting. This compensation range is specific to the locality of the job. The actual salary paid to an individual will be based on multiple factors, including, but not limited to, specific skills, education, licenses or certifications, experience, market value, geographic location, and internal equity. Although we estimate the successful candidate hired into this role will be placed between the entry point and the middle of the range, the decision will be made on a case-by-case basis related to these factors.​ This job is also eligible to participate in PG&E’s discretionary incentive compensation programs.

A reasonable starting salary range is: Bay Area Minimum: $159,000

Bay Area Maximum: $236,500

Job Responsibilities

Quantitative Risk Analytics and Mitigation Evaluation: Apply mathematical, probabilistic, and statistical techniques to objectively quantify the likelihood and impact—financial, operational, safety, and reliability—of identified risks, including but not limited to cybersecurity, physical security, data security, and IT asset failure. Transition risk assessments from subjective ratings to monetized, objective, verifiable, and actionable values to support risk-informed decision-making, investment planning, and risk reduction tracking.

Data Management and Model Development : 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 to develop, validate, and visualize robust risk and mitigation models within the organizational environment. Lead the estimation of mitigation effectiveness, calculation of benefit-cost ratios, and evaluation of model assumptions, inputs, and methodologies.

Stakeholder Collaboration: Partner with subject matter experts, risk managers, and risk owners to develop credible risk models and integrate quantitative risk assessment into core business and operational processes.

Risk Analytics Leadership : Mentor and guide junior staff and risk analysts, standardizing processes and tools across the data science function. Collaborate with analytics platform owners to prioritize and advance scalable risk and mitigation modeling capabilities. Assess and enhance existing risk modeling methodologies to drive continuous improvement.

Communication: Prepare and deliver clear, concise documentation and presentations on data sources, methodologies, analyses, results, and validations. Produce model documentation, whitepapers, formal reports, and expert testimony as required.

Qualifications

Minimum:

Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics, or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field

8 years in data science (or 2 years, if possess Doctoral Degree or higher, as described above)

Desired:

Doctoral Degree or higher in Data Science, Machine Learning, Computer Science, Physics, Econometrics, or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field

Relevant industry experience (electric or gas utility, cybersecurity, analytical consulting, etc.), 8 years

Experience in quantifying cybersecurity risk using the FAIR framework (certification preferred)

Experience in quantitative risk analysis or Probabilistic Risk Assessment

Strong understanding of the mathematical, probabilistic and statistical foundations that underpin data science and risk modeling

Proven proficiency in Monte Carlo simulation methods, Bayesian inference, and application of data science and operations research methodologies and tools

Demonstrated expertise in advanced programming, especially in Python; and proficiency in utilizing Git in a team environment

Excellent analytical, problem-solving, research and organizational abilities; attention to detail

Proficiency in synthesizing complex information into clear insights and translating those insights into decisions and actions

Proficiency in model lifecycle management

Strong data management knowledge, including governance, security, and quality best practices

Expertise in data visualization and communicating risk-related modeling results

Proven ability to work independently, proactively improve methods, and adapt to change

Effective communication and collaboration skills with diverse teams and stakeholders

Competency in project management and strong ability to manage multiple tasks under tight deadlines

Current knowledge of industry trends and issues, demonstrated through professional contributions

Ability to translate complex technical insights for various audiences and mentor others