Overview
Data Scientist – W2 Jobs in Washington, DC at Aroha Technologies, Inc
Title: Data Scientist – W2
Company: Aroha Technologies, Inc
Location: Washington, DC
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.
