Data Scientist, Principal

Requisition ID # 169304

Job Category: Accounting / Finance

Job Level: Manager/Principal

Business Unit: Operations - Other

Work Type: Hybrid

Job Location: Oakland

Department Overview

The aim of the Electric System Predictive Analytics team in the Wildfire Mitigation organization is to enhance the risk practices of PG&E's Electric Operation business and thereby address changing external conditions such as climate change. To this end the Electric System Predictive Analytics team enhances and maintains predictive models of electric system failures. These models help to provide a multi-layered view of risk across the electric system so that decision-making processes include and empower employees at all levels of the company to manage risk appropriately.

Sample activities include:

Development of new Machine Learning (ML) models predicting distribution and transmission electric system failures.

Development of physics-based models predicting distribution and transmission electric system failures.

Support for stakeholders in how to integrate model predictions into business operations.

Position Summary

The purpose of this role is to improve PG&E's ability to quantify the risk of wildfires caused by the distribution system. The successful candidate will design and develop models and improve existing models to quantify the likelihood of outages and ignitions. They will own key decisions about model algorithms and data pipeline architecture. The candidate will engage in internal and external communities of practice in data science, artificial intelligence, and machine learning to drive innovation and share knowledge. Additionally, they will educate non-technical stakeholders on the benefits, limitations, and maturity of data science solutions.

This position is hybrid, working from your remote office and your assigned work location based on business need. The assigned work location will be within the PG&E Service Territory. This position will require travel to Oakland once per month.

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 towards the middle or entry point of the range, the decision will be made on a case-by-case basis related to these factors.

Bay Minimum: $159,000

Bay Maximum: $271,000

&/OR

CA Minimum: $151,000

CA Maximum: $257,000

This job is also eligible to participate in PG&E's discretionary incentive compensation programs.

Job Responsibilities

Creates, applies, and evaluates advanced data mining architectures / models / protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets

Applies and evaluates 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.

Writes and documents complex and reusable python functions as well as multi-modular python code for data science.

As a technical leader, provides thought leadership in the use of a ML algorithms for solving business problems.

Mentors junior data scientists and drives standardization in process and toolsets across the data science community at PG&E.

Collaborates with analytics platform owners to prioritize and drive development of scalable data science capabilities.

Acts as peer reviewer for complex models/AI algorithm proposals.

Recognizes and prioritizes the most important work related to data science models to achieve highest operational and strategic impact for analytics in the business.

Works with enterprise leaders as an advocate for digital transformation of the business through the adoption of data science, analytics, and data-driven business processes.

Presents findings and makes recommendations to executive leadership and cross-functional management.

Qualifications

Minimum:

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

8 years in data science OR 2 years, if possess Doctoral Degree or higher, as described above

Desired:

Doctorate Degree in Data Science, Machine Learning, or job-related discipline or equivalent experience

Experience in utility and energy industries

Thought leadership in the external data science/artificial intelligence/machine learning community of practice, as demonstrated through peer reviewed journal publications, intellectual property/patent achievements, conference presentations, volunteering in professional organizations for the advancement of the field, participation in externally sponsored research projects, open source contributions, or similar activities.

Proficiency with data science standards and processes (model evaluation, optimization, feature engineering, etc) along with best practices to implement them.

Proficiency with commonly used data science programming languages, packages, and software tools for building data science/machine learning models and algorithms

Mastery in explaining in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.

Ability to clearly communicate complex technical details and insights to colleagues, stakeholders, and leadership

Leadership in developing, coaching, teaching and mentoring others to meet both their career goals and the organization goals