Requisition ID # 157227
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. EORM organization is charged with overseeing all operational risk management related to PG&E’s operations and public safety including evaluating risks associated with wildfires, nuclear, dams, natural gas, cyberattacks and natural disasters.
Position Summary
The Principal Data Scientist will support the enterprise risk analytics function of providing quantitative risk analysis and modeling for effectively managing a variety of enterprise and operational risks that PG&E face. The work that the position performs will inform important decisions at PG&E and support various regulatory filings such as Risk Assessment and Mitigation Phase (RAMP), General Rate Case (GRC), Wildfire Mitigation Plan (WMP). You will work on continuous improvement of quantitative assessment of risk and its mitigations, and evolvement of the analytical tools (data processing, algorithms, python codes, excel files, foundry, etc) that enable consistent and useful evaluation of the risks and mitigations across the company. You will be responsible for designing, developing, and executing scripts, programs, models, algorithms, and processes, using structured and unstructured data from disparate sources and sizes, generating actionable insights for strategy and policy development, process improvement, and product enhancement. You will also work on technical development phases: data engineering, analytics/modeling, and visualization/user interface; interact with technical and non-technical clients to resolve analysis and technical issues; and work with teams, clients, and senior leadership throughout the development cycle practicing continuous improvement. You will also review and validate existing methods, assumption, algorithms and models used and work on improving and advancing risk analytics at PG&E.
This position is hybrid, working from your remote office and Oakland General Office 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 salary range is:
Bay Area Minimum: $159,000
Bay Area Maximum: $271,000
Job Responsibilities
- Work closely with domain experts to develop relevant domain knowledge in the electric and gas utility, as well as knowledge of related datasets.
- Gather, clean, transform, and/or reduce data from dissimilar sources from across PG&E.
- Work with business partners to advance business processes, based on analytical findings.
- Apply machine learning and other analytical modeling methods to develop robust and reliable analytical models, including visualizations, within PG&E’s software development environment.
- Document data sources, methodology, and model evaluation metrics.
- Mentor junior data scientists, analysis and modeling risk analysts, risk analysts and drive standardization in process and toolsets across the data science community at PG&E
- Collaborate with analytics platform owners to prioritize and drive development of scalable data science and risk and mitigation modeling capabilities at PG&E
- Manage the development of complex quantitative models and tools
- Validate risk models, inputs, outputs, algorithms, and codes
- Assess existing methods used in risk and mitigation modeling at PG&E and advocate, drive, and implement evolution and improvements
- Recognize and prioritize the most important work related to risk analytics to achieve highest operational impact and drive risk-informed decision making
- Utilize deep understanding of business and risk drivers and financial levers to provide strategic decision support
- Assess business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures and processes, and advanced data analysis.
- Present findings and make recommendations to executive leadership and cross-functional senior management
- Discuss and explain the risk analysis and modeling methodology to internal and external stakeholders including presenting to public workshops
- Research the quantitative methods and findings related to risk and mitigation analysis relevant for PG&E
Qualifications
Minimum:
- Doctoral degree in computer science, econometrics, economics, engineering, mathematics, applied sciences, statistics, machine learning or job-related discipline
- 4 years of job-related experience
Desired:
- Relevant industry experience (electric or gas utility, analytical consulting, etc), 8 years
- Experience in quantitative risk analysis or Probabilistic Risk Assessment
- Strong foundation of probability, probability distributions, statistics and risk analysis
- Competent programming skills in a language especially in Python and familiarity with Git
- Demonstrated experience of Monte Carlo simulation methods and models
- Familiarity and experience with Bayesian statistics and inference
- Ability to work independently and proactively and to take initiative to improve analytical methods or processes
- Proficiency with the elements of the data management lifecycle (data acquisition, security, storage, architecture, integration, governance, compliance, reference data management, data quality and metadata) and best practices
- Experience with the elements of Model Lifecycle Management
- Experience using retrieving data from structured database using SQL
- Strong Excel and PowerPoint skills
- Strong data visualization skills and techniques for communicating risk-related data and modeling results in a clear and visually compelling manner
- Strong problem-solving, analytical, and organizational skills with attention to detail
- Ability to share knowledge, information and progress with the team effectively and efficiently
- Effectively copes with change, makes decisions and acts without having complete information and comfortably handles risk and uncertainty.
- Ability to research and apply knowledge, skills, and techniques to risk analysis
- Ability to manage conflicts in a positive, non-abrasive manner
- Ability to communicate with peers, leadership, and stakeholders in a clear and effective manner
- Highly motivated and self-driven
- Ability to collaborate with people with diverse background and skillsets
- Ability to effectively manage and prioritize multiple and diverse tasks and adhere to tight deadlines
- Knowledge of the industry trends and current issues in job-related area of responsibility as demonstrated through peer reviewed journal publications, conference presentations, open source contributions or similar activities
- Competency with relevant project management tools, theories and techniques as needed to support the timely and successful execution of project requirements
- Competency with commonly used data science and/or operations research programming languages, packages, and tools
- Proficiency in synthesizing complex information into clear insights and translating those insights into decisions and actions
- Ability to clearly communicate complex technical details and insights to colleagues, stakeholders and leadership
- Knowledge of the mathematical and statistical fields that underpin data science
- Ability to develop, coach, teach and/or mentor others to meet both their career goals and the organization goals
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