Data Scientist, Expert

Pacific Gas And Electric Company•Oakland, CA

1d•Hybrid

About The Position

The Applied Data Science team within the Wildfire Mitigation organization aims to enhance the risk practices of PG&E’s Electric Operation business, addressing evolving external conditions like climate change. The team focuses on integrating predictive models of wildfire and electric reliability risk into data products for operational decision-making and reporting. These products offer a multi-layered view of risk and risk reduction across the electric system, empowering employees at all levels to manage risk appropriately. Sample activities include quantifying wildfire mitigation program performance, developing models to predict outage duration, and supporting stakeholders in integrating model predictions into business operations. The Data Scientist, Expert applies, operationalizes, and executes scripts, programs, models, algorithms, and processes using structured and unstructured data to generate defensible, valid, scalable, reproducible, and documented machine learning and artificial intelligence models for problem-solving and strategy development. This role translates predictive insights into actionable decisions by integrating existing and emerging models into production workflows, decision frameworks, and risk management processes. The position also involves participating in internal and external data science communities to advance knowledge and educating non-technical communities on the advantages, risks, and maturity levels of data science solutions. This is a hybrid position, requiring work from both a remote office and an assigned location based on business need.

Requirements

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

Nice To Haves

Doctorate Degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent experience
Expertise in experimental design and causal inference methods.
Relevant industry experience (electric or gas utility, data science consulting, etc.)
Demonstrated experience applying advanced analytics or machine learning model in operational, planning , or regulatory decision contexts.
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
Ability to develop, coach, teach and/or mentor others to meet both their career goals and the organization goals

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 model execution and analysis
Develops large scale datasets and analytical products for use across PG&E’s Electric Operation business
Wrangling and prepares data as input of machine learning model development and feature engineering
Translates model outputs into metrics, dashboards, and applications usable by non-technical stakeholders
Architects, develops, and documents reusable python functions and modular python code for data science.
Conducts risk-evaluation studies of model impact on business outcomes, and documents results for leadership review and regulatory reporting
Assesses business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures and processes, and advanced data analysis.
Works with stakeholder 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