Data Scientist

1. Serves as a visible ambassador for AI and data science across the organization.


2. Develops, validates, and deploys analytics & data science models and agentic AI solutions using structured and unstructured data to address business challenges across operations, asset management, finance, and other functional areas.


3. Designs proof-of-concept frameworks with defined success criteria and evaluation metrics; scales successful POCs into repeatable, production-grade pipelines within data platform.


4. Translates model outputs into actionable decision-support tools and communicates findings clearly to both technical and non-technical audiences, including senior leadership.


5. Collaborates with data engineering to define data ingestion, feature engineering, and model serving requirements; documents model logic, assumptions, data dependencies, and performance benchmarks.


6. Partners with business and technology stakeholders to identify, scope, and deliver data science and AI use cases; provides technical assessments of vendor-built models and third-party analytics tools as needed.

Specific Job Requirements

• Successful completion of pre-employment drug, alcohol, and background investigation.


• Ability to build trust and rapport quickly with employees at all levels, demystifying AI for non-technical audiences, and championing responsible adoption through day-to-day partnership.


• Strong programming skills in data science and AI/ML languages (e.g., Python or R), with fluency in core ML and AI libraries such as scikit-learn, XGBoost/LightGBM, and pandas.


• Skilled at leveraging pre-built and foundation models rather than building from scratch where appropriate; demonstrated ability to design and build agentic AI solutions (e.g., tool use, retrieval, multi-step orchestration) with appropriate guardrails and evaluation.


• Proficiency with SQL and cloud data platforms (e.g., Azure, Databricks, or equivalent); comfort working with large-scale structured and semi-structured datasets.


• Deep understanding of model validation, evaluation metrics, feature engineering, and ability to identify and mitigate data leakage.


• Ability to explain findings, model performance and results, and recommendations clearly to diverse audiences and translate business problems into analytical frameworks.


• Ability to preserve confidential and proprietary information and successfully avoid conflicts of interest.


• Experience working effectively in cross-functional teams and establishing positive working relationships across functions and levels.


• Experience with MLOps/AIOps practices, version control tools (e.g., GitHub, Azure DevOps), Agile software development best practices, AI-assisted coding tools (e.g., Claude Code) to accelerate development; and data visualization platforms (e.g., Power BI, Tableau) preferred.


• Must be able to prioritize, manage multiple workstreams, and recognize when to seek direction.