What are the responsibilities and job description for the Data Scientist position at Sriven Systems Inc.?
Job Title: Data Scientist
Location: Hybrid Richmond, VA (Local candidates preferred)
Duration: Contract -W2
Overview
Dominion Energy is seeking a highly skilled Data Scientist to join their Enterprise Data Analytics team. This role focuses on delivering advanced analytics and machine learning solutions to support grid modernization, operational efficiency, and clean energy initiatives. The ideal candidate will operate as a senior individual contributor and technical consultant, working closely with cross-functional business units.
Key Responsibilities
- Partner with business units (Generation, Transmission & Distribution, Grid Operations, Asset Management, Customer Operations, Finance) to identify high-impact data science use cases
- Design, build, and deploy predictive, prescriptive, and diagnostic models
- Develop solutions for:
- Predictive maintenance & asset health
- Load forecasting & demand modeling
- Outage prediction & restoration optimization
- Grid resilience & renewable energy integration
- Customer analytics & energy efficiency programs
- Apply advanced techniques such as time series forecasting, anomaly detection, optimization, clustering, NLP, and statistical modeling
- Build end-to-end data science solutions from data ingestion to deployment and monitoring
- Implement MLOps best practices (CI/CD, model versioning, automated testing, deployment)
- Monitor model performance, detect drift, and optimize models in production
- Collaborate with data engineers and cloud teams for scalable deployments
- Develop dashboards and visualization tools for business stakeholders
- Mentor junior team members and contribute to analytics best practices
Required Skills & Experience
- 5 years of hands-on experience in Data Science using Python and/or R
- Strong expertise in machine learning, statistical modeling, forecasting, and optimization
- Experience working with large, complex datasets (structured, semi-structured, unstructured)
- Hands-on experience with time series data, sensor/SCADA data, or similar datasets
- Proven ability to translate analytical insights into business recommendations
- Experience building dashboards and applications (Power BI, R Shiny, Streamlit, Dash, etc.)
- Knowledge of MLOps practices (MLflow, Dataiku, Azure ML, CI/CD pipelines)
- Experience with model monitoring, explainability, and performance tracking
- Familiarity with data engineering concepts (data pipelines, feature engineering, data quality)
Preferred Qualifications
- Experience with cloud platforms (AWS, Azure, Google Cloud Platform), Snowflake, or Databricks
- Exposure to energy/utilities domain or industrial data environments
- Understanding of data governance, security, and regulatory compliance