Senior Data Science Analyst

This range is provided by CEI. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

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

Work Schedule
:
Alternating weekly schedule of 5 days onsite & 5 days remote | Minimum 40 work hours per week.

Duration/Length of Assignment
: 12 Month Contract to Hire

Additional Information
:
Candidates will go through the process of obtaining an Export Control Clearance prior to start

* 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
:

We are supporting a leading utility organization in the recruitment of two Senior Data Science Analysts to join an enterprise analytics group supporting company-wide strategic initiatives. These roles are being added as part of an effort to strengthen the company’s data-driven decision-making capabilities and optimize operational efficiency through advanced machine learning and predictive analytics. The position is part of a cross-functional analytics team composed of data scientists, engineers, and business stakeholders working collaboratively to deliver actionable insights for critical business functions.

The environment supports independent project ownership while maintaining a strong peer-review culture and consistent team collaboration. In this position, the analyst will lead and execute high-visibility data science projects from end to end. This includes collecting business requirements, conducting exploratory data analysis, designing and testing machine learning models, and presenting insights to senior stakeholders. Candidates are expected to work across departments on multiple analytics initiatives and demonstrate strong autonomy in applying their data science skills to solve complex business problems.

The role is highly collaborative, requiring clear communication, thoughtful documentation, and mentorship of junior analysts as needed.

Required Skills/Experience/Qualifications
:

• Bachelor’s degree or higher in Computer Science, Information Systems, or Mathematics
• Minimum 5 years of hands-on experience working as a data scientist using R or Python on Hadoop platforms
• Strong statistical modeling skills and advanced knowledge of R, Python, or both
• Demonstrated ability to design and lead data science projects that solve business problems through machine learning
• Practical expertise in predictive modeling, classification, regression, and clustering methodologies
• Proven experience working with Big Data Ecosystems, including Hadoop, HDFS, Hive, Sqoop, Spark, PySpark, SparkR, SparkSQL, Jupyter, and Zeppelin notebooks
• Proficient in working with both structured and unstructured datasets
• Excellent communication skills, both written and verbal, with the ability to deliver presentations to business and technical audiences
• Strong interpersonal skills and ability to work independently or in cross-functional teams

Preferred Skills (Not Required):

• Experience or working knowledge of data engineering principles or frameworks
• Familiarity with cloud technologies such as AWS, Azure, Google Cloud Platform (GCP), or Snowflake
• Prior experience working in large-scale utility organizations or capital project environments

Day to Day/Responsibilities
:

• Work independently to lead analytics projects that span across departments and impact operational decisions
• Conduct meetings with business stakeholders to gather analytic requirements and clarify project objectives
• Perform feature engineering and exploratory data analysis on complex datasets, including both structured and unstructured formats
• Apply statistical modeling and machine learning algorithms such as classification, regression, and clustering to…