Data Scientist (AI/ML)

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. The Data Scientist is responsible for the full data science lifecycle, including data acquisition, exploratory data analysis, feature engineering, model development, validation, deployment support, performance monitoring, and continuous improvement. This position applies predictive and prescriptive analytics, machine learning methodologies, and AI technologies to uncover insights, optimize business processes, improve operational performance, and support strategic initiatives. In addition to traditional machine learning techniques, the role contributes 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. The position also supports AI governance, model lifecycle management, and the integration of AI capabilities with enterprise systems and data assets. This is a highly analytical and hands-on technical role requiring strong data science fundamentals, critical thinking skills, business acumen, and the ability to translate complex analytical findings into actionable recommendations for stakeholders.

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.

Equivalent Experience: Bachelor’s Degree in Computer Science, Mathematics, Statistics, or a related field with five (5) or more years of experience in data science or applied machine learning, preferably in an electric utility or power systems environment.

Licenses, Certifications and/or Registrations: None required. Relevant certifications in cloud-based AI/ML platforms such as Microsoft Certified: Azure Data Scientist Associate or equivalent are a plus. Specialized Skills (e.g., typing, computers, software, tools and equipment uses, etc.):

• Working knowledge of machine learning techniques including linear and logistic regression, generalized additive models, clustering, decision tree learning, random forests, and neural networks, as well as an understanding of their practical strengths and limitations.
• Familiarity with model interpretability tools such as SHAP values is a plus.
• Strong programming skills in Python or PySpark required; experience with R or SAS is helpful.
• 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.
• 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.
• Awareness of responsible AI principles including model explainability, bias, and fairness considerations.
• Ability to communicate technical methods and findings clearly to non-technical audiences.
• Experience with data visualization tools such as Power BI or Tableau.
• A drive to learn and master new technologies and techniques.
• Proficiency in MS Office/365 Suite required.