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.