Associate Data Scientist

Associate Data Scientist

CFC's Data & AI unit is building production agentic systems that automate complex underwriting decisions—real AI agents and machine learning (ML) pipelines that drive business outcomes.

This Associate Data Scientist role is designed for someone early in their career who wants genuine exposure to production AI systems from day one. You will work closely with ML and AI engineers, contributing to discovery work that feeds directly into live services, including statistical analysis, feature engineering, LLM evaluation, and evidence-based thinking that ensures AI systems perform under real conditions.

The problems are genuinely hard: complex data, high-stakes decisions, and a domain with meaningful depth. If you want to grow quickly in applied AI and ML by working on real systems—not toy datasets—this is a strong environment.

Responsibilities

• Design and run experiments that directly shape production AI agents—testing ideas, validating approaches, and turning research into deployed improvements.
• Explore cutting-edge developments in AI and machine learning, with space and support to experiment, prototype, and bring new techniques into production where they add value.
• Evaluate and refine LLM-powered workflows by building robust evaluation frameworks, stress-testing agent behavior, and driving continuous quality improvements in live systems.
• Explore complex, real-world datasets to uncover insights that meaningfully improve underwriting decisions and system performance at scale.
• Prototype and iterate on features for AI/ML pipelines, moving from early exploration to measurable impact in production services.
• Investigate how agentic systems behave in production by identifying edge cases, failure modes, and opportunities to improve robustness and reliability.
• Collaborate closely with ML/AI engineers to bridge research and production—contributing code, debugging issues, and helping ship improvements end-to-end.
• Document experiments, findings, and methodologies clearly so that insights are reproducible and decisions are traceable.

Qualifications

• Enjoy solving complex problems and turning data into meaningful, real-world outcomes.
• Work across the full analytical lifecycle: exploring data, building machine learning models, and shaping how insights are communicated and applied across the business.
• Support responsible exploration and application of emerging technologies such as LLMs, focusing on performance and value in practice.
• Combine a strong grounding in statistics with hands-on technical capability in Python and SQL.
• Take pride in producing clear, reliable analysis.
• Be thoughtful and detail-driven, able to communicate effectively with a wide range of stakeholders, and comfortable learning from others while building ownership.
• Bring curiosity, ask the right questions, and look beneath the surface.
• Experience with Azure, MLflow, or LLM tooling is a bonus but not required.