Expert Data Scientist

Requisition ID # 164869 

Job Category: Accounting / Finance 

Job Level: Individual Contributor

Business Unit: Electric Engineering

Work Type: Hybrid

Job Location: Oakland

Department Overview

Electric Asset Management is responsible for the electric system engineering and planning, asset strategy, and risk management across transmission, distribution, and substation asset families. This centralized, risk-informed approach allows PG&E to manage electric risk, asset and system health, interconnections, and performance by using consistent standards, work methods, prioritization, and program sponsorship, while leveraging lessons learned from inspections and asset data to inform asset management decisions. The organization is accountable for asset planning and strategy, standards and work methods, and asset data management for Electric. The Asset Knowledge Management (AKM) team, within Electric Asset Management, is responsible for the management, quality, and access of PG&E’s electric asset data. The team’s objective is to maximize the use and ensure the trustworthiness of PG&E’s critical electric data assets.  

Position Summary

The Data Management & Analytics (DM&A) Product Development team develops client centered delivery of Electric data products that reduce risk and improve operations. The team is looking for a Expert level data scientist who thrives on driving engineering and asset management improvements and insights through strategic data science and analysis. The individual in this role must have demonstrated success with cross-functional projects, ability to communicate complex concepts to leadership across the organization, experience driving solutions, detailed oriented, and able to think strategically.

In this role, you will work closely with fellow product developers, product managers, and partners throughout the Electric organization to understand their needs to develop valuable products (full-stack development). You will be a thought leader, a tech lead and be instrumental in building processes, tools, libraries to improve the efficiency of the team. 

Designs, and develops, models, algorithms, and processes, using structured and unstructured data from disparate sources and sizes, generating actionable insights using data science and analytics. Works on process improvement, and product development/ enhancement. Works on technical development phases: data engineering, analytics/modeling, and visualization/user interface. Interacts with technical and non-technical clients to resolve analysis and technical issues. Works with product managers, team members, clients, and senior leadership throughout the development cycle. practicing continuous improvement

This position is hybrid, working from your remote office and your assigned work location based on business need.

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.
 
A reasonable salary range is:

Bay Area Minimum: $140,000
Bay Area Maximum: $238,000
 

Job Responsibilities

• Execute full stack analytic product development, through ideation, pipeline, modelling and user interfaces and documentation.
• Work closely with domain experts. Develop relevant domain knowledge in the electric utility.
• Understand and apply machine learning and other analytical modeling methods to develop robust and reliable analytical models, including visualizations, within PG&E’s software development environment.
• Mentor junior data scientists and data analysts and drive standardization in process and toolsets across the data science community at PG&E
• Collaborate with fellow developers within the team for development of scalable data science capabilities
• Defines, sources, implements, and documents robust, repeatable datasets for internal and cross-organizational use
• Acts as peer reviewer of complex models.
• Presents findings and makes recommendations to high level leaders.
• Works with sponsor departments and company subject matter experts to understand application and potential of data science solutions which create value.
• Compose and documents reusable python functions and modular python code for data science.
• Wrangles and prepares data as input of machine learning model development and feature engineering.
• Applies data science/ machine learning /artificial intelligence methods to develop defensible and reproducible predictive or optimization models which involve multiple facets and iterations in algorithm development.
• Extracts, transforms, and loads data from dissimilar sources from across PG&E for their machine learning feature engineering.
• Define, source, implement, and document robust, repeatable datasets for internal and cross-organizational use
• Creates advanced data mining architectures / models / protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets.
   

Qualifications

Minimum:

• 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, as described above)

Desired:

• Proficiency with the steps in the data science lifecycle:  data gathering and preparation, feature engineering, model development and model testing
• Proficiency with at least one commonly used data science programming language, such as Python, R, Matlab, Scala, or similar.  
• Expertise in data science/machine learning models and algorithms, such as Bayesian/statistical inference, NLP, deep learning, classification, clustering, forecasting, time series analysis, or other relevant techniques.
• Experience in utility and energy industries 
• Proficient in Palantir Foundry
• Proficient in image and natural language processing
• Experience with systems thinking and structuring complex problems
• Experience teaching and/or mentoring junior colleagues
• Experience with programming best practices, including documentation, version control (e.g., Git or equivalent), unit testing, etc.
• Knowledge of the mathematical and statistical fields that underpin data science, such as probability, statistics, optimization, linear algebra etc.
• Expertise with data science/machine learning models and algorithms, such as: Bayesian/statistical inference, NLP, deep learning, classification techniques, clustering techniques, forecasting, time series analysis, etc.
   

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