Sr. Data Scientist

Lincoln Financial•Radnor, PA

2d•$96,900 - $176,200•Hybrid

About The Position

Lincoln Financial Group is seeking a Data Scientist to join our AI Product & Delivery organization, focused on the agentic AI systems powering our next generation of products. You will generate insights from data, build and validate models, design experiments, and measure the business value AI agents deliver — while establishing rigorous evaluation frameworks that keep agentic systems accurate, safe, and reliable in a regulated financial services environment. You will partner closely with product owners, engineers, and business stakeholders to turn analysis into decisions and evals into guardrails.

Requirements

3–8 years of experience in data science, applied machine learning, or a related analytical role.
Hands-on experience building and evaluating ML models; experience with agentic AI systems (LLM agents, tool use, multi-step reasoning) strongly preferred.
Experience designing and analyzing experiments (A/B testing, causal inference, or similar).
Proficiency in Python, SQL, and standard ML/data science tooling.
Strong ability to communicate technical findings to non-technical stakeholders.
Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or a related field.

Nice To Haves

Experience building or evaluating LLM-based agents or multi-agent systems.
Familiarity with eval frameworks and observability tools (e.g., LangSmith, Weights & Biases, Ragas).
Experience with LLM APIs or enterprise AI platforms (e.g., Azure OpenAI, AWS Bedrock, Anthropic Claude, Google Vertex AI).
Advanced degree (Master's or Ph.D.) in a quantitative field.
Domain experience in life insurance, annuities, retirement planning, or employee benefits.

Responsibilities

Analyze usage, performance, and outcome data to surface actionable insights on how agentic AI features are used and where they fall short.
Translate findings into clear, actionable recommendations for product, engineering, and business stakeholders.
Build and maintain dashboards and reporting that track agent performance and business impact.
Design, build, and validate models and agentic workflows.
Evaluate model and agent architecture choices, balancing accuracy, latency, cost, and risk.
Collaborate with engineering to productionize models and agents and monitor them post-launch.
Design and run experiments — A/B tests, offline evaluations, holdouts — to test agent behavior, prompt or model changes, and feature variants.
Define hypotheses, success metrics, and sample size or power requirements; ensure statistical rigor.
Interpret results and translate them into clear go/no-go recommendations.
Define and track metrics that connect agentic AI features to business value — efficiency gains, cost savings, revenue, and customer or employee experience.
Build measurement frameworks that isolate AI-driven impact from other contributing factors.
Report on ROI and value realization to product and business leadership.
Design and maintain eval suites and benchmarks covering task success, reasoning quality, tool-use correctness, safety, and failure modes.
Build regression frameworks and test case libraries to catch performance degradation across model or prompt updates.
Partner with product owners on human-in-the-loop review processes and use eval findings to guide model and agent improvements.