Principal Data Scientist

Data Scientist, Principal

Location: Oakland, CA

Onsite Flexibility: Hybrid — Onsite ~1 day per week

Contract Details

• Position Type: Contract
• Contract Duration: 12 months
• Pay Rate: $150.00–$157.00 / Hour (USD)
• Work Authorization: Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

Job Summary

The Undergrounding Risk Management team within the Undergrounding & System Hardening organization aims to enhance the risk practices of the Electric Operation business and thereby address changing external conditions such as climate change. To this end, the Electric Risk Management & Analytics team develops, maintains, and applies predictive models to enable the organization to close the gap between metrics and electric system performance.

These models 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 predictive models using Python or PySpark and executed in Foundry or AWS.
• Interpretation and representation of meteorological data in models that combine a range of data sources such as the electric system asset data, vegetation, and meteorology.
• Designing statistical methodology and architecting programmatic solutions to utilize risk model outputs for business use cases.

The Principal Data Scientist leads the design, development, and execution of scripts, programs, models, user interfaces, 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. This role also educates the non-technical community on advantages, risks, and maturity levels of data science solutions.

Key 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 the organization 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.
• Architects, develops, and documents reusable functions and modular 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 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.
• Acts as peer reviewer of complex models.

Required Skills

• PySpark proficiency
• User interface development proficiency
• Strong cross-functional collaboration skills

Preferred Skills

• Expertise in experimental design and causal inference methods.
• Expertise in statistical methods for time series analysis, statistical modeling, and probabilistic risk assessment.
• Relevant…