Requisition ID \# 171676
Job Category: Accounting / Finance
Job Level: Individual Contributor
Business Unit: Energy Delivery
Work Type: Hybrid
Job Location: Oakland
Department Overview
The aim of the Applied Data Science 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. The team focuses on integrating predictive models of wildfire and electric reliability risk into data products for operational decision making and reporting. These products provide a multi\-layered view of risk and risk reduction 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:
Quantification of wildfire mitigation program performance on the distribution and transmission electric system.
Development of models to predict outage duration from electric system failures
Support for stakeholders in how to integrate model predictions into business operations.
Position Summary
Applies, operationalizes, and executes scripts, programs, models, algorithms, and processes, using structured and unstructured data from disparate sources and sizes, generating defensible, valid, scalable, reproducible and documented machine learning and artificial intelligence models (predictive or optimization) for problem solving and strategy development. Translates predictive insights into actionable decisions by integrating existing and emerging models into production workflows, decision frameworks, and risk management processes. Participates in internal and external communities of practice in data science/artificial intelligence/machine learning to advance knowledge in the field. Educates the non\-technical community on advantages, risks, and maturity levels of data science solutions.
This position is hybrid, working from your remote office and your assigned 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.
Bay Minimum: $140,000
Bay Maximum: $238,000
This job is also eligible to participate in PG\&E’s discretionary incentive compensation programs.
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 model execution and analysis
Develops large scale datasets and analytical products for use across PG\&E’s Electric Operation business
Wrangles 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
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:
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