Data Scientist

Xcel Energy•Minneapolis, MN

4d•$97,600 - $138,600•Onsite

About The Position

The Data Scientist, Electric Distribution & Gas Integrated Planning is responsible for applying advanced analytics, data science, and modeling techniques to support long-term planning of electric and natural gas systems. This role works within Integrated System Planning (ISP) to transform complex datasets into actionable insights that inform infrastructure investment decisions, resource planning strategies, regulatory filings, and emerging energy transition initiatives. The position supports the integration of electric and gas planning activities by developing data-driven analyses related to load growth, beneficial electrification, distributed energy resources, customer adoption trends, system reliability, affordability, and decarbonization pathways. This involves leveraging statistical analysis, machine learning techniques, and data visualization tools to create models and algorithms that provide actionable insights. The role aligns with ISP's mission of coordinating long-term plans across electric and gas systems and supporting integrated, data-driven planning frameworks.

Requirements

Bachelor’s degree in computer science, data science, math, statistics, math, or related with an analytical concentration.
A minimum of 3 years working as a data scientist or application developer (2 years with MS or Ph.D).
Relevant certifications can be considered in lieu of a post graduate degree in combination with years of experience. (i.e. MLOps, data platform certs, etc.)

Nice To Haves

Experience in the regulated utility industry

Responsibilities

Collecting and cleaning data from various sources and ensuring data quality.
Manipulating and transforming data using tools like SQL, Python, and R.
Use statistical techniques and machine learning algorithms to perform exploratory data analysis to understand the nuances, patterns, and anomalies within the data.
Support development, validation, and deployment of machine learning & statistical models to predict, classify, and optimize various business outcomes.
Develop an understanding of delivering data science models in a production environment.
Creating impactful visualizations to communicate findings effectively.
Collaborating with stakeholders to understand business challenges and develop data-driven solutions to real-world problems.
Translating technical concepts into actionable insights.
Balancing learning with practical application.
Understanding project and team scope as it relates to broader strategic initiatives.
Effectively communicating findings to both technical and non-technical audiences.