Data Scientist - W2

Washington

**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.