Data Scientist

Job Description

Job Title: Senior Data Scientist – ML & Operational Analytics

Duration: Long-term

Work Schedule: Hybrid (Contract Full Time)

Location: 701 9th Street. Northwest Washington, DC 200608

Role Overview

The Senior Data Scientist – ML & Operational Analytics will sit on the business-facing side of data science, partnering directly with operational and infrastructure stakeholders to define problems, build machine learning solutions, and deploy models into production.

This role is not a backend data engineering or IT support position. It is a full‑lifecycle data science role focused on solving real business problems through predictive modeling, analytics, and AI.

You will support multiple initiatives across Safety and Infrastructure Analytics, with a heavy emphasis on asset health, reliability, efficiency, and operational performance. Approximately 60% of the role is new model development, with the remaining 40% enhancing and maintaining existing models.

Key Responsibilities

Machine Learning & Analytics

• Design, develop, and deploy machine learning models including regression, classification, and time‑series models for operational use cases.
• Apply advanced statistical and ML techniques to large‑scale datasets (terabytes to petabytes), including:
• Smart‑meter data
• Smart‑grid and IoT data
• Structured (relational databases)
• Unstructured data (text, documents, and limited multimedia)
• Perform feature engineering, data validation, and quality assessment to ensure model reliability and interpretability.
• Enhance existing models and pipelines while leading the development of net‑new solutions.
  

Business Partnership & Problem Solving

• Work directly with business stakeholders to:
• Identify operational problems
• Translate business needs into analytical frameworks
• Define success metrics and model outcomes
• Clearly communicate analytical findings, model results, and recommendations to non‑technical audiences.
• Validate insights with the business and iterate based on feedback.
• Own solutions end‑to‑end: problem → data → model → deployment → business adoption.
  

Data Science Lifecycle & Collaboration

• Collect, cleanse, standardize, and analyze data from multiple internal and external sources.
• Collaborate closely with:
• Information architects
• Data engineers
• Project and program managers
• Other data scientists and analysts
• Ensure smooth handoff and adoption of deployed solutions.
• Document methodologies, assumptions, and results to support governance and reuse.
• Act as a subject matter expert in machine learning, AI, feature engineering, data mining, and statistical modeling.
  

Required Qualifications

• MS degree in Computer Science, Statistics, Mathematics, Engineering, Physics, or a related quantitative field (or 15 years of equivalent professional data science experience)
• 5 years of hands‑on experience as a data scientist working on operational analytics or applied ML problems.
• Proven experience building and deploying ML models—not just training or research models.
• Strong proficiency in:
• Python (primary)
• R
• SQL
• Common ML libraries (e.g., scikit‑learn, statsmodels, etc.)
• Strong foundation in:
• Probability and statistical inference
• Regression techniques
• Experimental design and validation
• Demonstrated experience working closely with business stakeholders to deliver production solutions.
  

Preferred Qualifications

• PhD in Computer Science, Statistics, Mathematics, Engineering, Physics, or related field.
• Experience within an Electric Utility, Energy, Infrastructure, or Industrial environment.
• Hands‑on experience with Azure Machine Learning for model development and deployment.
• Knowledge of optimization techniques, including:
• Linear programming
• Mixed‑integer optimization
• Exposure to:
• Computer vision
• Generative AI use cases
• Azure certifications are a plus.