Data Science Lead

Berkshire Hathaway Energy•Des Moines, IA

About The Position

Collaborate with business stakeholders to identify high‑value use cases that improve operational efficiency, customer experience, reliability, safety, and sustainability across electric and gas operations. Translate use cases into technical roadmaps, quantifiable KPIs, and delivery plans. Design, develop, and deploy machine learning models (predictive, optimization, time‑series, NLP). Perform exploratory data analysis, feature engineering, and model validation. Lead AI solution engineering using Azure OpenAI Service (LLMs), retrieval‑augmented generation (RAG) with Azure Cognitive Search/Vector stores, prompt design, grounding with authoritative utility data, and guardrail policies. Build production‑grade AI microservices/APIs, orchestration, and monitoring on Azure, including hallucination mitigation, content filtering, and responsible AI checks. Establish end‑to‑end MLOps/LLMOps pipelines, automated testing, blue/green rollouts, drift detection, model performance/SLA dashboards, and rollback procedures. Build and maintain scalable, secure data pipelines to ingest, transform, and curate data from different systems. Partner with data engineers and architects to uphold data quality, lineage, governance (Unity Catalog), and security across the Azure ecosystem. Communicate findings and operational insights via Power BI dashboards and narrative data products, enabling decision‑makers in operations, planning, supply chain, and customer service. Contribute to best practices, reusable accelerators (feature stores, pipeline templates, model cards), code standards, and knowledge sharing within the Data & Analytics community of practice. Mentor and coach data scientists and engineers; conduct design reviews and provide technical oversight for complex initiatives. Ensure solutions align with utility regulatory, cybersecurity, and privacy requirements, and with corporate Responsible AI and model risk management policies. Partner with Security/Legal to complete risk assessments and approvals; incorporate auditability, explainability, and human‑in‑the‑loop controls.

Requirements

Master’s degree or foreign equivalent in Data Science, Computer Science, Artificial Intelligence, Mathematics or related field.
3 years of experience with Data science
3 years of experience with Data Modeling and Machine Learning modeling, or analytics
3 years of experience with Azure Machine Learning
3 years of experience with Azure Data Factory
3 years of experience with Azure Databricks
3 years of experience with Azure DevOps
3 years of experience with Azure AI Foundry
3 years of experience with Power BI
3 years of experience with Programming with Python and PySpark
3 years of experience with SQL
3 years of experience Working with large-scale datasets time series and Advanced Metering Data
3 years of experience with Git
3 years of experience with Agile
3 years of experience Working in the gas and/or electric utility industry; operational systems and data domains including AMI/MDM, OMS/SCADA/EMS, asset management, and market operations.
2 years of experience working with Large Language Models to develop Retrieval Augmentation Generation Systems.

Responsibilities

Collaborate with business stakeholders to identify high‑value use cases that improve operational efficiency, customer experience, reliability, safety, and sustainability across electric and gas operations.
Translate use cases into technical roadmaps, quantifiable KPIs, and delivery plans.
Design, develop, and deploy machine learning models (predictive, optimization, time‑series, NLP).
Perform exploratory data analysis, feature engineering, and model validation.
Lead AI solution engineering using Azure OpenAI Service (LLMs), retrieval‑augmented generation (RAG) with Azure Cognitive Search/Vector stores, prompt design, grounding with authoritative utility data, and guardrail policies.
Build production‑grade AI microservices/APIs, orchestration, and monitoring on Azure, including hallucination mitigation, content filtering, and responsible AI checks.
Establish end‑to‑end MLOps/LLMOps pipelines, automated testing, blue/green rollouts, drift detection, model performance/SLA dashboards, and rollback procedures.
Build and maintain scalable, secure data pipelines to ingest, transform, and curate data from different systems.
Partner with data engineers and architects to uphold data quality, lineage, governance (Unity Catalog), and security across the Azure ecosystem.
Communicate findings and operational insights via Power BI dashboards and narrative data products, enabling decision‑makers in operations, planning, supply chain, and customer service.
Contribute to best practices, reusable accelerators (feature stores, pipeline templates, model cards), code standards, and knowledge sharing within the Data & Analytics community of practice.
Mentor and coach data scientists and engineers; conduct design reviews and provide technical oversight for complex initiatives.
Ensure solutions align with utility regulatory, cybersecurity, and privacy requirements, and with corporate Responsible AI and model risk management policies.
Partner with Security/Legal to complete risk assessments and approvals; incorporate auditability, explainability, and human‑in‑the‑loop controls.