Senior Data Scientist ML & Operational Analytics
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Job Location:
Washington, AR - USA
Monthly Salary:
Not Disclosed
Posted on:
4 hours ago
Vacancies:
1 Vacancy
Job Summary
Senior Data Scientist - ML & Operational Analytics
Location: 701 9th Street. NW Washington DC 200608 (Local)
W2 only
Additional Notes
- Local to Washington DC is preferred (Pepco support).
- Open to candidates local to Philadelphia (#2) or Chicago (#3) offices.
- This role was previously misclassified as a Business Analyst-candidates with primarily IT ETL or ticket based backgrounds are not a fit.
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.
Ideal Candidate Profile
- A seasoned business oriented Data Scientist (not a BI analyst or backend data engineer).
- Comfortable owning ambiguity and defining problems-not just executing predefined tasks.
- Has worked on the business side delivering solutions that are used operationally.
- Confident saying:
Here is the problem here are the stakeholders here is the data and here is the deployed model that solves it. - Utility infrastructure or industrial data science experience aligns extremely well.
Must Have Candidate Criteria
Must have owned data science solutions end to end including defining the business problem and KPIs sourcing and validating data building and deploying ML models and supporting the solution after deployment in an operational environment.
Must have hands on experience building and deploying machine learning models for real operational use cases specifically regression classification and/or time series models with demonstrated feature engineering and appropriate model evaluation techniques.
Must be proficient in SQL Python R and data preparation for modeling including the ability to independently create training datasets perform data quality checks prevent data leakage and work with large complex datasets without relying on a separate data engineering team.
Must have a strong statistical foundation and validation mindset including experience with inference experimental or quasi experimental design regression analysis and clearly proving model impact beyond accuracy metrics.
Must have demonstrable experience partnering directly with business stakeholders translating ambiguous business problems into analytical solutions communicating findings to non technical audiences and influencing decisions based on data driven insights.
