Data Scientist - Energy Division

Colonial Group, Inc. Houston, TX

Company

Colonial Group, Inc.

Location

Houston, TX

Type

Full Time

Job Description

Colonial Energy, Inc. (CEI) competitively delivers reliable natural gas supplies to small and large business customers, utilities, power generators, and large industrial, institutional, and commercial customers in the Eastern United States. Colonial Energy, Inc. is a wholly-owned subsidiary of Colonial Group, Inc. Reporting to the Director of Trading – Natural Gas, the Data Scientist – Energy Division will play a critical role in transforming complex data into meaningful insights that drive strategic decision making across our Natural Gas and Oil businesses. This highly analytical and forward-thinking individual will leverage advanced analytics, machine learning and statistical modeling to enhance forecasting, trading strategies and operational efficiency. The Data Scientist – Energy Division will collaborate with energy traders, logistics teams and senior leadership to deliver both historical analysis and predictive insights, enabling smarter, data-driven decisions across the organization. JOB EXPECTATIONS/DUTIES • Analyze large scale datasets from CSV/API files and market platforms to uncover trends and anomalies. • Develop predictive models for demand forecasting, margins, load balancing, energy pricing, and asset/refinery maintenance. • Design algorithms to optimize energy usage, grid operations, power generation, and storage for energy; pipeline usage and demand date for refined products. • Collaborate with cross-functional teams including operations, trading, and financial planning. • Build dashboards and visualizations to communicate insights and support real-team decision making. • Implement data science solutions in cloud or on-prem environments and help scale models for production use. • Streamline daily operations/tasks where applicable. • Stay informed about the latest advances in data science, energy technology, and regulatory trends. • Ensure data integrity, governance, and compliance within energy industry standards. • Other projects and duties may be assigned.QUALIFICATIONS/REQUIREMENTS • Bachelor’s or Masters Degrees in Data Science, Statistics, Computer Science, or a related field. • 2+ years of experience delivering predictive models or analytics solutions in commercial or operational settings; energy industry related preferred. • Proficient in Python, R, or similar programming languages. • Strong knowledge of SQL and experience working with large, complex datasets. • Experience with machine learning frameworks and model developments. • Familiarity with time-series analysis and forecasting models. • Understanding of energy systems, electricity markets, or grid operations/refined products is a strong plus. • Proficiency in data visualization tools such as Power BI, Tableau, Looker. • Outstanding communication skills. • Proven ability to work effectively across cross-functional teams in a fast-paced corporate environment.Physical Requirements: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of the job. While performing the duties of this job, the employee is regularly required to sit, stand, walk, and may occasionally lift and/or move up to 40 pounds. Eligibility Requirements:Hiring is contingent upon eligibility to work in the United States. Colonial Group, Inc. is committed to creating an environment that values and supports diversity and inclusiveness across our organizations. We encourage applications from qualified individuals who will help us achieve this mission. Colonial Group, Inc. prohibits discrimination of and will receive consideration for employment without regard to race, color, religion, sex, age, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status.
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Date Posted

10/02/2025

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