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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Data Scientist - **Company:** Summit Utilities, Inc. - **Location:** Little Rock, AR, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Information Engineering, Data Profiling, Database Queries, Digital Assets, Supervisory Control and Data Acquisition (SCADA), Python (Programming Language), Machine Learning, NumPy, Query Optimization, Cloud Services, Standard Sql, Azure Machine Learning, Feature Engineering, Azure Data Factory, Snowflake, Git, Pandas, Matplotlib, Microsoft Fabric, Scikit Learn, Information Technology, Machine Learning Operations, Industrial Software, Azure Synapse Analytics, Software Version Control, Data Pipelines, Databricks - **Published:** May 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ba51b0aca980bb32 ## About the Role 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., * 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. ## Description 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., * 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. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Vectorize all the things! 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