AI Data Scientist

Join our Growing Team and see why Summit Utilities, Inc was named as one of the Fastest Growing Denver Area Private Companies 2019 and 2020; Best Places to Work in Maine 2019, 2020, 2021, 2022 and 2023; and Best Places to Work in Arkansas 2020 and 2023, Oklahoma 2022 and 2023 and Missouri 2023. Summit was also recently named one of Forbes 2023 America’s Best Small Employers.

Summit is a growing natural gas utility providing safe, reliable, and clean burning natural gas service to homes and businesses in Arkansas, Colorado, Maine, Missouri, Oklahoma, and Texas. Being part of the Summit team means embracing excellence and innovation, committing to safety each and every day, and doing all that we can to serve each other, our customers, and the communities where we live. We aim to bring warmth and energy to everything we do.

We have an exciting hybrid opportunity for an AI Data Scientist based in Fort Smith, Little Rock, or Fayetteville, Arkansas.

POSITION SUMMARY

Summit Utilities is seeking a Data Scientist II to develop and deploy data science solutions that support safe, reliable, and efficient natural gas utility operations. Data Scientists at this level work with moderate guidance to build, validate, and communicate statistical, machine learning, and mathematical optimization model insights across a range of operational and business use cases, including field operations optimization, asset health/preventative maintenance and damage prevention prediction, climate analysis/weather normalization, gas supply planning, usage forecasting, and customer segmentation modeling. 

This role sits within the AI and Digital Assets organization, working directly with the AI and data engineering team and collaborating with business partners across Operations, Customer Care, Regulatory, Finance, and IT. Ideal candidates bring 2–4 years of applied data science experience, strong Python and SQL skills, and knowledge of the Microsoft Fabric ecosystem (or similar). Candidates with strong backgrounds in statistics, mathematics, operations research, and computer science will be considered. 

PRIMARY DUTIES AND RESPONSIBILITIES

• Build, validate, and iterate on mathematical optimization, statistical, and machine learning models supporting natural gas demand forecasting, weather normalization and climate analytics, asset health/preventative maintenance, field operations optimization, and customer segmentation-type use cases. 


• Perform feature engineering on datasets including capital and O&M work order data, weather and climate data, SCADA readings, predictive maintenance and damage information, pipeline telemetry, billing data, and gas usage.


• Conduct exploratory data analysis (EDA) and data profiling to assess data quality and identify patterns in large, complex datasets. 


• Contribute to end-to-end model development pipelines within the Microsoft Fabric environment and support development of our MLOps platform.


• Develop clear, accurate quantitative summaries using Python visualization libraries to communicate findings to business partners. 


• Support data quality checks and validation of model inputs and outputs to ensure analytical integrity. 


• Collaborate with Data Engineers to translate analytical requirements into structured data pipeline needs. 


• Document models, analytical logic, data sources, and assumptions to support maintainability and cross-team understanding. 


• Stay current on developments in data science methods, natural gas industry analytics, and the Microsoft Fabric and Azure ML tooling ecosystem.

 EDUCATION AND WORK EXPERIENCE

• Bachelor’s degree in data science, Statistics, Mathematics, Operations Research, Computer Science, Engineering, or a related quantitative field required; master’s degree preferred. 


• 2–4 years of applied experience in data science, machine learning, or quantitative analytics roles 


• Demonstrated experience building and validating statistical, optimization, and machine learning models through the entire model lifecycle from development to production. 


• Experience working with large, complex datasets from operational or industrial systems preferred, natural gas or energy utility experience a plus. 


• Familiarity with Microsoft Fabric, Azure Data Factory, Azure Synapse Analytics, or equivalent cloud data platforms, such as Snowflake and Databricks

 KNOWLEDGE, SKILLS, ABILITIES 

• Proficiency in Python for data analysis and machine learning, including pandas, NumPy, scikit-learn, and visualization libraries (matplotlib, seaborn) 


• Strong SQL skills including complex joins, aggregations, and query optimization 


• Demonstrated proficiency in machine learning model development lifecycle: feature engineering, training, validation, evaluation, and deployment. 


• Strong knowledge of Bayesian statistics, time-series analysis, and weather-driven forecasting methods, with exposure to techniques such as ARIMA or similar approaches 


• Familiarity with Microsoft Fabric workspace navigation, Lakehouses, and notebook-based development; Snowflake or AWS/Azure ecosystem experience accepted as equivalent.


• Proficiency with source control tools and collaborative development practices (Git, AzureDevOps)


• Friendly, solution-oriented team player aligned with Summit’s PEAKS values.

The above statements are intended to describe the general nature and level of work being performed by employees assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties and/or skills required of all personnel so classified.

Summit offers competitive pay and medical/dental/vision and other benefits that provide flexibility, choice and support to our employees when they need it most. We understand that home and family are essential pieces of your life, and our benefits are designed to support you both at work and at home.

Summit Utilities, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or protected veteran status and will not be discriminated against on the basis of disability or veteran status.