Senior Data Scientist

One of CEI's largest Energy, Power, & Electric Utilities clients is seeking a Sr. Data Science Analyst to join their growing organization!

Client/Industry: Energy, Power, & Electric Utilities

Job Title: Senior Data Science Analyst

Location: Hybrid - (5 days in office, 5 days remote, repeating) | Richmond, VA 23219

Work Schedule/Shift: Mon-Fri | Minimum 40 work hours per week.

Duration/Length of Assignment: 12 Month Contract to Hire

Additional Information: Candidates must undergo Export Control Clearance. Preference for U.S. Permanent Residents (Green Card holders) or U.S. Citizens.

*Must be able to convert to a full-time employee without sponsorship, restrictions, or an additional employer*

• W2 Employment Only – No Corp to Corp / C2C arrangements.
• Expected potential for contract extension(s) and/or conversion to Full-Time/Permanent Employment.
• Optional benefits available during contract (Medical, Dental, Vision, and 401k)

Position Overview:

This Senior Data Scientist role supports a major enterprise initiative focused on advancing data-driven solutions across the energy sector. The position was created as part of a strategic program designed to optimize operational efficiencies and improve analytical decision-making across departments. The role sits within a high-functioning analytics team that operates across platforms and supports multiple departments with tailored data science models and tools. The team is structured with senior and junior data scientists collaborating on complex projects, with direct communication and alignment to leadership and technical stakeholders. The selected candidate will lead and contribute to a wide range of projects involving the application of statistical models, machine learning techniques, and custom analytic solutions to business problems. They will be responsible for designing, developing, and validating predictive models and analytical systems. The position requires ownership of projects from end to end—beginning with requirements gathering and ending with actionable insights delivery—while maintaining collaborative interaction with business users and data engineers throughout the process.

Required Skills/Experience/Qualifications:

• Bachelor’s degree or higher in Computer Science, Information Systems, Mathematics, or a related field
• Minimum of 5 years of hands-on experience in Data Science with Python and/or R on Hadoop platforms
• Advanced knowledge of machine learning, data mining, predictive modeling (classification, regression, clustering), and statistical methods
• Proven experience developing machine learning models and feature engineering on both structured and unstructured data
• Proficiency in R or Python for statistical modeling and ML prototyping
• Experience with Big Data tools and platforms such as Hadoop, HDFS, Hive, Sqoop, Spark (pySpark, SparkR, SparkSQL), and Jupyter/Zeppelin notebooks
• Demonstrated ability to create interpretable data visualizations and present analytical insights to both technical and non-technical stakeholders

Preferred Skills (Not Required):

• Experience with data engineering concepts and workflows
• Familiarity with cloud environments including AWS, Azure, GCP, or Snowflake
• Prior exposure to working within the energy, utilities, or infrastructure sector
• Experience mentoring or guiding junior data science team members

Day to Day/Responsibilities:

• Conduct business consultations and lead requirements gathering sessions to define use case objectives and scope
• Perform advanced data analysis, including feature engineering and transformation of datasets
• Design, develop, and refine machine learning models for tasks including classification, regression, and clustering, aligning to business goals
• Work independently on complex analytic problems using procedures and platforms built on R, Python, and Hadoop
• Develop and maintain data structures and integration pipelines that support machine learning and predictive modeling
• Use Jupyter and Zeppelin notebooks for experimentation and documentation of analytic workflows
• Utilize Spark, pySpark, SparkSQL, and SparkR to manage and analyze large datasets
• Validate and compare model outputs before deployment, ensuring interpretability and accuracy of results
• Create and present visualizations that communicate insights and provide actionable recommendations
• Lead or mentor less experienced data science team members, offering guidance on methodology and approach
• Provide oral briefs and presentations to internal teams and stakeholders, translating complex analytics into digestible outcomes
• Collaborate across teams in a multi-project environment, managing timelines and deliverables while supporting enterprise-level goals