Knights of Columbus

Data Scientist

• Support end-to-end delivery of AI solutions: participate in problem framing, exploratory analysis, model development, and measurement of outcomes
• Develop and validate machine learning models under guidance (e.g., classification/regression, time series, anomaly detection, NLP as appropriate)
• Contribute to GenAI solutions by building and improving Retrieval-Augmented Generation (RAG) components (document preparation, chunking, metadata, and retrieval testing) and prompt templates following established patterns
• Perform data analysis and feature engineering: data quality checks, joins, transformations, leakage checks, and creation of reusable features
• Define and track model/LLM quality metrics with support (e.g., accuracy, calibration, stability; groundedness/factuality for LLM outputs), and help maintain evaluation datasets and test cases
• Assist with explainability and documentation: summarize model behavior, limitations, and key drivers; contribute to model cards and audit-ready artifacts
• Work with data engineers and platform teams to operationalize solutions: support pipeline integration, scheduling, monitoring, and troubleshooting
• Apply responsible AI practices: follow privacy/security requirements, handle sensitive data appropriately, and participate in bias/fairness reviews
• Communicate findings clearly: prepare concise readouts, visuals, and recommendations for technical and non-technical audiences

Applied AI, Data Scientist

• Develop and deploy advanced statistical, machine learning, and AI models supporting insurance, actuarial, claims, and investment decision-making.
• Design and deliver LLM-enabled analytics and Deep Research solutions, including RAG-based approaches over structured and unstructured data.
• Perform hands-on data analysis, feature engineering, and model development using enterprise platforms (Snowflake, PostgreSQL, DB2).
• Define model success metrics, validation approaches, and explainability standards aligned to business and regulatory expectations.
• Partner with data engineers and architects to produce models, establish monitoring, and manage model lifecycle and drift.
• Apply responsible AI practices including bias assessment, transparency, and documentation for audit and compliance review.
• Communicate analytical insights and model outcomes clearly to business leaders and technical stakeholders.
• Collaborate across analytics, engineering, risk, and compliance teams to ensure scalable, governed AI and analytics delivery.

Applied AI, Data Scientist

Director of Applied AI, Data Science