Requisition ID # 160965 

Job Category: Accounting / Finance 

Job Level: Individual Contributor

Business Unit: Information Technology

Work Type: Hybrid

Job Location: Oakland

 

 

Department Overview

 

The Enterprise Data Science & Artificial Intelligence (EDS&AI) department 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, artificial intelligence (AI), machine learning (ML) and Generative AI (GenAI) technologies to drive PG&E’s transition to the sustainable grid of the future. EDS&AI works cross-functionally across the company to enable data driven decisions applying advanced analytics & AI techniques, as well as improvements to relevant business processes. Deployed to some of PG&E’s highest priority arenas, EDS&AI 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 in DS&AI team, this Department listens to the needs of AI/ML/GenAI and data science 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

 

We are seeking two (2) experienced data scientist to join PG&E as expert members of company’s Center of Excellence in Data Science & AI. Each technical leadership role has an unparalleled opportunity to influence the success of data science and related AI technical fields (ML and GenAI) across a large, diverse company with an essential societal mission. Each role is perfectly suited for a candidate with deep experience leading technical portfolios focusing on AI/ML/GenAI modeling work, and who would now like to make a contribution to the data science community by supporting teams across the company rather than delivering solutions directly. A desire and aptitude for consulting, mentoring, advising, educating, influencing, and relationship building is strongly needed. This technical leader will be an individual contributor who possesses a drive to keep their skills sharp through continuous education and research as they assist with a wide variety of advance data science and analytics projects across the company. Study and continuous assessment of emerging technologies will facilitate thought leadership and influence technical roadmaps.

 

Strictly data science-AI/ML/GenAI algorithm-related software engineering skills are desired with experience developing production-ready code and managing data science products through the delivery cycle. Emphasis will be placed on a knowledge of and experience with algorithms, theory, measurement and evaluation, coding, tooling, data systems, and support environments specific to data science and AI. Successful candidates may come from diverse backgrounds including data science, ML engineering, software engineering, physical sciences, and others (education requirements listed below). This role will also make contributions to the development and implementation of standards, processes, governance frameworks, guidelines, and operational product excellence (i.e., AI/ML/GenAI product lifecycle evaluation, along with scalability, validity, and other performance measures). Finally, another critical component of this profile is AI/ML/GenAI model evaluation and performance assessment when vendors or third-party developers formulate proposals or develop solutions for internal Functional teams.

 

The role is hybrid working from your remote office and in-person based on business needs or company requirements.

 

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:             $136,000

Bay Area Maximum:            $232,000

 

Job Responsibilities

 

As part of the centralized Hub team, support data science, AI/ML/GenAI and advanced analytics Spokes across the company by spearheading the implementation of best practices in the development of AI/ML/GenAI and other advanced data science and analytics models, in aspects such as code engineering and best practices in coding, statistical and probabilistic problem modeling, product scalability, AI/ML/GenAI model evaluation, and the like. Support the design, implementation, and continuous improvement of governance tools (policies, standards, and processes) for the effective and safe development of AI/ML/GenAI and data science models as a product. Continuously educate Spokes on governance requirements. Monitor compliance and escalate as needed. 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 technology regulations at the state and national level to pivot internal compliance. Develop and maintain an emerging technologies evaluation toolkit to assess technology maturity level and its readiness for value realization of business goals. Current focus of said toolkit revolves around Generative AI and automated decision making by AI algorithms, monitoring elements such as model hallucinations and misinformation, training biases, etc. In cross-collaboration with the Enterprise Strategy and Architecture team, Functional Areas, and other players in product acquisition, support vendor and third-party proof of concept proposal and product assessment from a technical perspective, mainly (but not exclusively) on AI/ML/GenAI model design and approach, performance measure metrics, technical maturity, etc. Support 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. Continuous dissemination and active participation in the internal and external data science community of practice, leading the development of knowledge that advances the field. Leading emerging communities of practice (such as the Generative AI CoP) to disseminate knowledge and generate networks internally and externally. Advise and consult 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 (EDS&AI) level. Support the adoption of Lean methodologies across EDS&AI. Support building data science capability by advising Hub teams as well as Spokes across the enterprise, with the end goal to contribute to improved decision-making in all functional areas. Present findings and make recommendations to officers and cross-functional management. Educate the internal community (including Executives) on emerging trends. Continuously monitor new technologies and assess their impact and potential disruption to business programs. Effectively communicate a compelling vision of data science and AI/ML/GenAI technologies that add value to the company. Build and maintain strong relationships with business units and external agencies.

Additional General Job Responsibilities

 

Researches and applies advanced knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions. Creates advanced data mining architectures / models / protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets. Extracts, transforms, and loads data from dissimilar sources from across PG&E for their machine learning feature engineering. Applies data science/ machine learning /artificial intelligence methods to develop defensible and reproducible predictive or optimization models that involve multiple facets and iterations in algorithm development. Wrangles and prepares data as input of machine learning model development and feature engineering. Writes and documents reusable python functions and modular python code for data science. Assesses business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures and processes, and advanced data analysis. Works with sponsor departments and company subject matter experts to understand application and potential of data science solutions that create value. Presents findings and makes recommendations to senior management. Acts as peer reviewer of complex models.

Qualifications

 

Minimum Education:

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

Desired Education:

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

Minimum Work Experience:

6 years in data science (or no experience, if possess Doctoral Degree or higher, as described above).

Desired Work Experience:

Relevant industry (electric or gas utility, renewable energy, analytics consulting, etc.) experience

Knowledge, Skills, Abilities and (Technical) Competencies:

Active participation in the external data science/artificial intelligence/machine learning community of practice, as demonstrated through volunteering in professional organizations for the advancement of the field, presentations in conferences or publications to disseminate data science knowledge and topics, or similar activities. Competency with data science standards and processes (model evaluation, optimization, feature engineering, etc.) along with best practices to implement them. Knowledge of 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 commonly used data science and/or operations research programming languages, packages, and tools for building data science/machine learning models and algorithms. Proficiency in explaining in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines. Mastery in clearly communicating complex technical details and insights to colleagues and stakeholders. Mastery 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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