Expert Data Scientist

Requisition ID # 158063 

Job Category: Accounting / Finance 

Job Level: Individual Contributor

Business Unit: Gas Engineering

Work Type: Hybrid

Job Location: San Ramon

Department Overview

Gas Operations is focused on ensuring the safe and reliable flow of natural gas to our customers. As a whole, Gas Operations is responsible for all aspects of PG&E’s gas distribution and transmission operations, including planning, engineering, maintenance and construction, restoration and emergency response.

Gas Transmission Operationsis responsible for maintaining over6,000 miles of gastransmissionpipelinesthroughoutCalifornia.ThisDepartment is responsible fortheoveralladministrationand implementationof theTransmissionIntegrity Management Program and theevaluationof overall risk to thegastransmission system. Thisincludesoverseeingthe completion of integrity management assessments,identifying High Consequence Areas (HCA), maintaining PG&E's assessmentplanas required by 49 CFR Subpart O, and managing PG&E's overallgas risk management program.

The Integrity Management organization's vision is to:

• Improve pipeline safety and system reliability with a goal of zero safety incidents.
• Promote a safety culture throughout all levels of the organization with an emphasis on improving learning from the past and anticipating the future.
• Apply integrity management principles on a system-wide basis while engaging our stakeholders, from local communities we operate in to our regulators, so they can understand and participate in reducing our risk.
• Support the design, construction, operation and maintenance of the transmission and distribution pipeline systems through the proactive use of asset knowledge, threat identification, knowledge of threat interaction, data integration and analytical tools to increase operational efficiency, improve system integrity and minimize safety risk to employees and the communities we operate in.

Position Summary

This Expert Data Scientist position will work on a cross-functional team of Risk Engineers, Risk Analysts, GIS Specialists, and Program Managers to provide business intelligence support involving the use of probabilistic risk modeling methodology for the implementation of risk management duties for PG&E’s gas transmission system. This position will also work collaboratively with data collection organizations to provide data quality assessment and feedback, assess fitness of data for risk modeling, research and adopt industry best practices, develop improvement strategies, and present results to senior leadership and regulatory agencies. This position will report to a Supervising Engineer of Risk Management within the Gas Transmission Integrity Management organization. The Expert Data Scientist is a key position within the Transmission Integrity Management Program (TIMP) team to help ensure PG&E maintains safe and reliable pipelines which comply with federal regulations.

Position duties may include (but are not limited to)-

• Apply data science/ machine learning /artificial intelligence methods to develop defensible and reproducible predictive or optimization models.
• Wrangles and prepares data as input of machine learning model development and feature engineering.
• Writes and documents python code for data science (feature engineering and machine learning modeling) independently.
• Assesses business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures and processes, and advanced data analysis.
• Extracts, transforms, and loads data from dissimilar sources from across PG&E for their machine learning feature engineering.
• Serves as the technical lead for the development of simple models.
• Acts as peer reviewer of simple models.
• Develops and presents summary presentations to management.
• Keeping up to date with industry innovations, benchmark with industry peers
• Collaborate with cross-functional teams to develop enterprise level vision for risk assessment.

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 (San Ramon, CA).

Expected travel- attend monthly in-person team meetings; minor travel to the field (less than 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 Area Minimum:$136,000

Bay Area Maximum:$232,000

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

Qualifications-

Minimum Qualifications:

• Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.
• 6 years in data science (or no experience, if possess Doctoral Degree or higher).

Desired Qualifications:

• Doctoral Degree or higher in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.
• 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, such as R, NumPy, Analytica, RapidMiner, SAS, Anaconda, MS Azure, Amazon, MatLab, Tableau, etc. Excellence in at least one of these is highly desirable.
• Mastery of the mathematical and statistical fields that underpin data science.
• Relevant industry (electric or gas utility, renewable energy, analytics consulting, etc.) experience
• Understanding of Department of Transportation (DOT) 49 CFR Part 192 codes and regulations, California Public Utility Commission (CPUC) GO 112E
• Mastery in clearly communicating complex technical details and insights that adapts to the unique needs of different audiences.

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.

•     Act as peer reviewer of complex models