Senior Data Science Analyst

Pre-Screen Questions: Must complete attached document and submit with your candidate submittal

Job Title: Data Scientist

Max Supplier Bill Rate: ***

Primary location of assignment: Thomas F Farrell Building

How many contractors are you needing? 1

What is the preferred candidate location (local, non-local, remote?) and is there flexibility? Local or drive in candidates ONLY, No 100% remote

If you are open to looking at non-local candidates will per diem be offered? No

What schedule is the candidate required to work: Alternate weeks in Richmond VA office; other week remote. (5 days in office, 5 days remote, repeating). No 100% remote

Are any certification required: No

Required Skills and Experience
1) MUST have prior hands on experience as a Data Scientist on a project using Python or R
2) Proven ability to translate complex analytical findings into clear, actionable insights for business leaders, engineers, operations teams, and executives
3) Experience designing, developing, and deploying advanced analytics and machine learning solutions aligned to business objectives
4) Ability to create clear, interpretable visualizations that tell a compelling story, support decision making, and align with executive level messaging
5) Demonstrated experience creating interactive dashboards, reports, and applications (e.g., RShiny, Power BI, Streamlit, Dash) for business consumption
6) Strong experience working with structured, semi structured, and unstructured data (e.g., sensor/SCADA data, time series data, text, images)
7) Expertise across machine learning, statistical modeling, forecasting, optimization, and anomaly detection, with real world application experience
8) Experience or working knowledge of MLOps practices including model development lifecycle management, automated testing, CI/CD pipelines, version control, and deployment (e.g., MLflow, Dataiku, Azure ML, or similar tools)
8) Strong understanding of model monitoring, including performance tracking, explainability, bias detection, model drift, and reproducibility in production environments
9) Experience or working knowledge of data engineering concepts, including data ingestion, transformation, feature engineering, and data quality controls
10) Experience with cloud and modern analytics platforms (AWS, Azure, GCP, Snowflake, Databricks, or similar) is a strong plus
11) Understanding of governance, security, and regulatory requirements for enterprise and utility data environments is preferred

What soft skill requirements do you have (team fit and personality requirements)?
o Strong communication skills both verbal and written
o Ability to lead, collaborate, or work effectively in a variety of teams, including multi-disciplinary teams

Nice to Have Skills:
Understanding and/or Experience with data engineering is a plus
Experience with cloud technologies(AWS, Azure, GCP, Snowflake) is big plus

High Level Project Overview:
This role serves as a technical consultant and senior individual contributor within ***s Enterprise Data Analytics team, delivering advanced analytics and data science solutions that support operational reliability, grid modernization, customer experience, and clean energy initiatives.

Key responsibilities include:
Partner with business units such as Generation, Transmission & Distribution, Grid Operations, Asset Management, Customer Operations, and Finance to identify high value data science use cases
Design, build, and deploy predictive, prescriptive, and diagnostic models to support:
o Asset health and predictive maintenance
o Load forecasting and demand modeling
o Outage prediction, restoration optimization, and reliability analytics
o Grid resilience, renewable integration, and emissions reduction initiatives
o Customer behavior, billing, and energy efficiency programs
Apply advanced techniques such as time series forecasting, survival analysis, optimization, clustering, NLP, and anomaly detection to utility scale data
Develop end to end data science solutions, from data acquisition and feature engineering to model deployment and post production monitoring
Support implementation of MLOps best practices to ensure scalable, reliable, and auditable analytics solutions in compliance with enterprise and regulatory standards
Collaborate closely with data engineers, platform teams, and cloud architects to ensure models are production ready and performant
Evaluate model performance continuously, identify data/model drift, and recommend retraining or enhancement strategies
Build reusable analytical frameworks and accelerators that improve time to value across the Enterprise Analytics portfolio
Create intuitive visualizations, dashboards, and self service analytics tools that empower stakeholders to explore insights independently
Mentor junior data scientists and analysts, contributing to analytics standards, code quality, and best practices
Support ***s commitment to safety, reliability, affordability, and clean energy transformation through responsible and ethical use of data and AI

Required Years of Experience:
MUST have 5+ years of experience in Data Science using Python or R, with a strong focus on analyzing large, complex, and high volume datasets

Education:
Education: Bachelors or higher required
Discipline: Computer Science, Information Systems, Mathematics

Are there any specific companies/industries youd like to see in the candidates experience?
High Preference for candidates that have previously worked with a large scale commercial utilities team but will review candidates who have a background with large scale capital projects for companies

Preferred Interview Process Overview (High level):
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