Principal Data Scientist

Requisition # 169267

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. The 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.

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, licences 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

• 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:
  Coll…