Data Scientist (AI/ML)

About the role

The Data Scientist (AI/ML) leverages advanced analytics, statistical modeling, machine learning, and artificial intelligence techniques to drive data-informed decision-making across the organization. This role partners with business leaders, functional teams, and technology stakeholders to identify opportunities, solve complex business problems, and develop scalable analytical solutions that create measurable business value.

Responsibilities

• Identify and apply predictive and prescriptive analytics, machine learning methodologies, and AI technologies to uncover insights, optimize business processes, improve operational performance, and support strategic initiatives.
• Contribute to the organization's AI strategy through the development and implementation of Large Language Model (LLM) solutions, retrieval-augmented generation (RAG), knowledge graph technologies, and emerging agentic AI frameworks.
• Support AI governance, model lifecycle management, and the integration of AI capabilities with enterprise systems and data assets.
• Translate complex analytical findings into actionable recommendations for stakeholders.

Requirements

• Education: Graduate Degree in Computer Science, Data Science, Mathematics, Statistics, Electrical Engineering, or a related quantitative field required.
• Experience: Two (2) to Four (4) years of experience working as a data scientist or in a closely related role, preferably with an electric utility or power systems environment.
• Programming skills: Strong programming skills in Python or PySpark required; experience with R or SAS is helpful.
• Tools and Technologies: Familiarity with MLOps tools such as MLflow and deep learning frameworks such as TensorFlow or PyTorch; experience with Databricks, Synapse Analytics, or a similar Apache Spark platform preferred; familiarity with Microsoft Azure and SQL Server is helpful.
• Specialized Skills: Working familiarity with large language model (LLM) concepts and prompt engineering; familiarity with methods for connecting AI models to company data including retrieval-augmented generation (RAG) or knowledge graphs; exposure to vector databases or similarity search tools such as Chroma, FAISS, or Azure AI Search is a plus; familiarity with Model Context Protocol (MCP) servers and how they are used to connect AI models to external tools and data sources; a willingness to learn and work with MCP-based integrations as the team adopts them is expected; familiarity with agentic AI frameworks such as LangChain or AutoGen is a plus; proficiency with Git and version control for collaborative development; familiarity with REST APIs and how models are exposed for use in applications; experience with FastAPI or similar is a plus.
• Communication: Ability to communicate technical methods and findings clearly to non-technical audiences.
• Software: Proficiency in MS Office/365 Suite required.

Qualifications

• Relevant certifications in cloud-based AI/ML platforms such as Microsoft Certified: Azure Data Scientist Associate or equivalent are a plus.
• Data Visualization: Experience with data visualization tools such as Power BI or Tableau.
• Learning: Drive to learn and master new technologies and techniques.