Manager, Reliability Data

Requisition ID # 172440 Job Category: Accounting / Finance Job Level: Manager/Principal Business Unit: Strategy & Growth Work Type: Hybrid Job Location: Oakland Department Overview The System Performance, Reliability and Resiliency Strategy team within the overall Electric Transmission and Distribution Engineering organization is responsible for planning, organizing, and managing the resources necessary to successfully execute PG&E’s Electric Reliability Strategy and initiatives. Within this department the Reliability Data team is on point for a key role is developing and curating all reliability data and data pipelines so that they meet auditable standards. Position Summary Leads the Reliability Data function within the System Performance, Reliability and Resiliency Strategy team, responsible for developing and executing advanced data science, machine learning (ML), optimization, simulation, and artificial intelligence (AI) solutions that enable future-focused, data-driven decision-making. Oversees the development and curation of enterprise reliability data and data pipelines, ensuring they meet rigorous audit, governance, and regulatory standards in support of PG&E’s Electric Reliability Strategy and initiatives. Directs the design, development, and integration of scalable data and analytics solutions that leverage large, complex datasets from multiple systems to support reliability performance, risk assessment, and strategic investment decisions. Builds and sustains deep technical expertise in data science, mathematics, statistics, and data engineering, while advancing capabilities across the full data lifecycle, including data ingestion, transformation, modeling, and visualization. Partners with engineering, operations, and senior leadership to align data and analytics solutions with enterprise reliability and resilience objectives. Drives continuous improvement in data quality, pipeline performance, and analytical methods to enhance transparency, traceability, and decision quality. This position follows a hybrid work model, requiring employees to report to their assigned office location at least two or three days per week. The remaining days may be worked remotely, depending on business needs. The headquarters is located in the Oakland General Office. PG&E is providing the salary range that the company, in good faith, believes it may pay for this position at the time of the job posting. This compensation range is specific to the job's locality. 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, collective bargaining agreements, and internal equity. Although we estimate the successful candidate hired into this role will be placed toward the middle or entry point of the range, the decision will be made on a case-by-case basis based on 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: $167,000Bay Area Mid-Point: $225,000Bay Area Maximum: $284,000 Job Responsibilities • Manages data scientist teams to accomplish results through effective recruitment and selection, training and development, performance management and coaching, and rewards and recognition.• Works with enterprise leaders to identify and solve business problems requiring the implementation of data science, machine learning, and artificial intelligence. • Utilizes deep understanding of business drivers and financial levers, along with instrumental data science techniques, to prioritize work and provide strategic decision support.• Ensures compliance with standards and processes to improve the quality and timeliness of machine learning/artificial intelligence/optimization models.• Acts as a peer reviewer for code scripts and model development for narrow scope projects. Reviews and approves the maturity for release of technical features in data science products.• Conducts risk-evaluation studies of model impact on business outcomes.• Assesses business implications associated with modeling assumptions, types of inputs, statistical methodologies, programming approach, etc.• Monitors the development of data science, machine learning, artificial intelligence, mathematical modeling and optimization, and similar emerging technologies to assess their impact on business strategies continuously. Participates with peers in risk and maturity assessment of data science tools.• Develops budget (expense, capital, and expenditures) and monitors, forecasts, and reports on budget performance. Qualifications Minimum: • Bachelor's degree in Statistics, Mathematics, Applied Science, Data Science, Engineering, Physics, Economics, or equivalent field.• 2 years of hands-on experience in data science (or no experience, if you possess an advanced degree, as described above)• 2 years of leadership experience in data science Desired: • Advanced degree in Statistics, Mathematics, Applied Science, Data Science, Engineering, Physics, Economics, or equivalent field.• Utility industry experience: 3 years Knowledge, Skills, Abilities, and Competencies: Experience with data science and machine learning algorithms (supervised, unsupervised), ML domains (computer vision, NLP, etc.). Experience with the following: Statistics: statistical modeling, experimental design, sampling, clustering, data reduction, confidence intervals, testing, predictive modeling, and other related techniques Artificial Intelligence: machine learning, predictive analytics, as they collect, analyze, and extract value out of data; simulation Software Engineering: programming languages, big data wrangling packages, cloud services, APIs, and related tools Making sense of complex, high-quality, and sometimes contradictory information to solve problems effectively. Ability to clearly and concisely communicate and present complex analysis to both quantitative and non-quantitative audiences. Ability to apply project management theories, concepts, methods, best practices, and techniques as needed to perform at the job level. Domain expertise: familiarity with one or more lines 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)