Senior Data Science Analyst

Pre-Screen Questions - List these at the end of candidate resume
1. Do you have at least 5 years of hands on experience working as a Data Scientist using Python or R on production projects involving large, complex, high volume datasets? Yes/No - If yes, briefly describe the types of datasets and use cases.
2. Have you designed and deployed end to end data science solutionsfrom data acquisition and feature engineering through model deployment and post production monitoring? Yes/No - Must include real production deployment, not only experimentation.
3. Which of the following techniques have you applied in real world projects? Please select all that apply
a. Time series forecasting
b. Machine learning / statistical modeling
c. Optimization
d. Anomaly detection
e. NLP or unstructured data analysis
4. Do you have hands on experience with MLOps practices such as model versioning, CI/CD pipelines, automated testing, and production monitoring (e.g., MLflow, Azure ML, Dataiku, or similar)? Yes/No - Briefly list tools used.
5. Have you regularly translated complex analytical results into clear, actionable insights for non technical stakeholders (business leaders or executives), including dashboards or visualizations? Yes/No - Brief example required.
6. Have you developed interactive with tools like RShiny, Power BI, Streamlit, Dash for business consumption? Yes/No - Brief example required, with tools used

Required Emphasis on:
Experience designing, developing, and deploying advanced analytics and machine learning solutions aligned to business objectives
Expertise across machine learning, statistical modeling, forecasting, optimization, and anomaly detection, with real world application experience
Develop end to end data science solutions, from data acquisition and feature engineering to model deployment and post production monitoring
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

Required Skills and Experience
MUST have prior hands on experience as a Data Scientist on a project using Python or R
Proven ability to translate complex analytical findings into clear, actionable insights for business leaders, engineers, operations teams, and executives
Ability to create clear, interpretable visualizations that tell a compelling story, support decision making, and align with executive level messaging
Demonstrated experience creating interactive dashboards, reports, and applications (e.g., RShiny, Power BI, Streamlit, Dash) for business consumption
Strong experience working with structured, semi structured, and unstructured data (e.g., sensor/SCADA data, time series data, text, image
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)
Strong understanding of model monitoring, including performance tracking, explainability, bias detection, model drift, and reproducibility in production environments
Working knowledge of data engineering concepts, including data ingestion, transformation, feature engineering, and data quality controls
Experience with cloud and modern analytics platforms (AWS, Azure, GCP, Snowflake, Databricks, or similar) is a strong plus
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)?
Strong communication skills both verbal and written
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:
Asset health and predictive maintenance
Load forecasting and demand modeling
Outage prediction, restoration optimization, and reliability analytics
Grid resilience, renewable integration, and emissions reduction initiatives
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
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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