Principal Data Scientist

Principal Data Scientist

Location: Oakland, CA

Onsite Flexibility: Hybrid — approximately 1 day per week onsite

Contract Details

• Position Type: Contract
• Contract Duration: 12 months (with potential for extension/conversion)
• Pay Rate: $150.00–$157.00 / Hour (USD)
• Travel Requirements: Not required
• 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 Operations 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 — significant development done in PySpark for modification of cost risk metrics.
• User Interface (UI) development proficiency — creating UIs on various risk tools to allow teams to design system hardening and cost-benefit analysis of GIS projects.
• Strong cross-functional collaboration skills.
• Proficiency with Palantir Foundry.
• Familiarity with GIS interface tools (e.g., SINS or similar).
• Ability to come up with ideas for how to implement solutions and design the framework.
• Hands-on development expertise to create user interfaces.

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 industry experience (electric or gas utility, data science consulting, etc.).
• Familiarity with the use of supervised, unsupervised, deep learning & physics-based methods for modeling electrical infrastructure failure modes.
• 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 Agile product development best practices.
• Proficiency with Python or PySpark, code reviews, and code development best practices.
• Proficiency in explaining in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, and 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's goals.

Education Requirements

• Master's degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field — required.
• Doctoral degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field — preferred.

Required Experience

• 8 years of experience in Data Science; OR 2 years of experience if the candidate possesses a Doctoral Degree or higher in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field.
• Experience working with or for consulting companies that served utility/energy clients is a plus.

Benefits

• Medical, Vision, and Dental Insurance Plans
• 401k Retirement Fund

Important Notes

• Local candidates only. Candidate must be local to the service territory.
• Given the heavy analysis required, a virtual desktop environment will not be suitable; a laptop will be provided upon start (or within a few days). If delayed, a personal device may be used via Citrix/VDI.
• Interview process: 2 rounds.

About GTT

GTT is a minority-owned staffing firm and a subsidiary of Chenega Corporation, a Native American-owned company in Alaska. We highly value diverse and inclusive workplaces and support Fortune 500 organizations across banking, financial services, technology, life sciences, biotech, utilities, and retail sectors throughout the U.S. and Canada.

Job Number: 26-08591 Industry: Data & Analytics