Requisition ID # 158646 

Job Category: Accounting / Finance 

Job Level: Senior Manager

Business Unit: Information Technology

Work Type: Hybrid

Job Location: Oakland

Department Overview

The Enterprise Data Science (DS) & Artificial Intelligence (AI) organization is both a “Delivery” team that is a sophisticated practitioner of data science and a “Center of Excellence” team that supports other practitioners in an enterprise-wide Hub & Spoke analytics adoption model.

As a Delivery team, this Department uses industry leading data science and change management practices to drive PG&E’s transition to the sustainable grid of the future. The Department works cross-functionally across the company to enable data driven decisions applying analytics, as well as improvements to relevant business processes. Deployed to some of PG&E’s highest priority arenas, the Department does not specialize in a traditional utility domain, such as asset management or program administration, but instead specializes in extracting useful insights from disparate data sets and facilitating actions informed by these insights.

As a Center of Excellence team, this Department listens to the needs of data science and AI practitioners across the company, along with emerging industry practices, and builds standards, processes, tools, knowledge and best practices that meet the current and future needs of the enterprise.

This team works on a wide variety of difficult problems, offering great variety in the work, and constant opportunity to explore and learn. Current and past engagements include:

• Creating wildfire risk models that are used by regulators and the utility to prioritize asset management.
• Developing computer vision models that improve, accelerate, and automate asset inspections processes.
• Predicting electric distribution equipment failure before it occurs, allowing for proactive maintenance.
• Forming the analytical framework behind PG&E’s Transmission Public Safety Power Shutoff
• Optimizing non-wires alternative resource portfolios, like the Oakland Clean Energy Initiative, including location and resource adequacy considerations.
• Analyzing customer demographic, program participation, and SmartMeter interval data to build program targeted propensity models, e.g. for customer owned distributed energy resource technologies.
• Identifying and investigating anomalous customer natural gas usage, in order to resolve dangerous customer side leaks.

Position Summary & Job Responsibilities Specific to the Role:

PG&E is looking for an experienced Senior Manager Data Scientist to lead the AI & Data Science Center of Excellence for the company. In this position, the successful candidate will play a vital role developing policies, standards, and processes that internal data science teams as well as contracted vendors will comply to for the effective design and development of data science and AI algorithms.  Additionally, and in partnership with the People’s Organization (HRBP & Compensation), this role will also be key in the evaluation of enterprise data science skills and knowledge to help build trust of those competencies for internal decision-making processes as the industry evolves. The incumbent will have an unparallel opportunity to lead frameworks and guidelines for governance as well as operational and product excellence (i.e. AI product lifecycle evaluation, along with scalability, validity, and other performance measures), and also provide the thought leadership in emerging technologies that will influence delivery of solutions across the company. Because of that, this role is perfectly suited to someone who has large experience leading data science AI/ML modeling work and would like now to make a contribution to the data science community as a technical expert and thought leader, managing a small team of data scientists who drive best practices in decision making excellence in the company, rather than delivering solutions themselves directly.  

As a leader of the centralized AI & Data Science Center of Excellence Hub, the responsibilities this incumbent include:

• Support data science, AI, and analytics Spokes across the company by disseminating and supporting the implementation of best practices for machine learning (ML) & AI development, in aspects such as code engineering and best practices in coding, statistical and probabilistic problem modeling, product scalability, and the like.
• Lead the design, implementation, and continuous improvement of governance tools (policies, standards, and processes) for the effective and safe development of ML & AI as a product. Continuously educate Spokes on governance requirements. Monitor compliance and escalate as needed.
• Additional focus on leading the development of governance documents (policy, standards and processes) for emerging and disruptive technologies such as Generative AI, Foundational Models, automation and hyper-automation technologies, etc. Keeping abreast of existing AI and other emerging technologies regulations at the State and National level to pivot internal compliance.
• Act as advisor to the People’s Organization (HRBP & Compensation) in the analysis of data science competencies, continuously monitoring the evolution of the industry and advising on skill fitness to current data science work planned enterprise-wide.
• Actively participate in the internal and external data science community of practice, leading the development of knowledge that advances the field.
• Advise delivery teams in the optimal implementation of advanced technologies as proof of concept, balancing risk and innovation to accomplish business goals.
• Support the identification and implementation of process improvement at the department level.
• Support building data science capability both within the Hub and, mainly, Spokes across the enterprise to contribute to improved decision-making in all Functional areas.
• Present findings and makes recommendations to officers and cross-functional management.
• Educate the internal data science/AI community on emerging trends. Continuously monitor new technologies and assess their impact and potential disruption to business programs.
• Effectively communicate a compelling vision of ML & AI technologies that add value to the organization.
• Build and maintain strong relationships with business units and external agencies.

This position is hybrid, working from your remote office and the Oakland General Office (OGO) 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:    $167,000   

Bay Area Maximum:   $284,000

Reporting Relationship

This position reports to the Director, Data Science & AI Governance.

Job Responsibilities (generic to the role):

• Develops and builds high performing teams by setting goals, developing, and managing work resources, and ensuring the team has adequate tools, training, and technology to successfully deliver outcomes.

• Works with enterprise leaders to identify and solve complex business problems requiring the implementation of data science, machine learning and artificial intelligence.

• Ensures standards and processes are implemented to improve quality and timeliness of machine learning/artificial intelligence/optimization models.

• Develops and implements frameworks to validate models, methodologies, as well as communicate results.

• Acts as peer reviewer for complex code scripts and model development for broad scope projects. Reviews and approves the maturity for release of technical features in data science products.

• Conducts risk-evaluation studies on machine learning/artificial intelligence/optimization model impact of business outcomes. Assesses risk and maturity assessment of data science standards and tools.

• Participates in the community of practitioners that co-creates the development of data science, artificial intelligence, mathematical modeling and simulation, and similar emerging technologies to continuously assess their impact on business strategies

• Develops budget (expense, capital, and expenditures) and monitor, forecast and report on budget performance.

Education Minimum

Masters degree in Statistics, Mathematics, Applied Science, Data Science, Engineering, Physics, Economics, or equivalent field.

Experience Minimum

• 3 years hands-on experience in data science 

• 3  years of leadership experience in data science

Experience Desired

• Utility industry experience: 5 years

Knowledge, Skills, Abilities and (Technical) Competencies

• Demonstrated track record and understanding of data science and machine learning algorithms (supervised, unsupervised), ML domains (computer vision, NLP, etc.).

• Demonstrated track record in the following:

a) Statistics: statistical modeling, experimental design, sampling, clustering, data reduction, confidence intervals, testing, modeling, predictive modeling and other related techniques;

b) Artificial Intelligence: machine learning, predictive analytics, as they collect, analyze and extract value out of data; simulation;

c) Software Engineering: programming languages, big data wrangling packages, cloud services, APIs, and related tools.

• Seeing ahead to future possibilities and translating them into breakthrough strategies.

• Ability to clearly and concisely communicate and present complex analysis to both quantitative and non- quantitative audiences.

• Competency in planning and prioritizing work to meet commitments aligned with organizational goals.

• Domain expertise: familiarity with one or more line of business (electric, customer, generation, procurement, gas, risk, etc.) and ability to identify areas where data science can improve processes and inform decision making (this may also include familiarity with the datasets/databases that support these lines of business)

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