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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Data Scientist - **Company:** Lincoln National Corporation - **Location:** Radnor, PA, United States - **Experience:** Expert - **Salary:** $96,900.0 - $176,200.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Python (Programming Language), Machine Learning, Software Product Management, SQL Databases, Test Case, Large Language Models, Grafana, Multi-Agent Systems, Model Validation, AI Platforms, Information Technology, Virtual Agents - **Published:** August 2, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=95b2d7dcca259caa ## About the Role 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. Grade level: final grade level for this position will be determined based on the selected candidate's experience. Nice to haves (preferred) 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. Application Deadline ## Description 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. What you'll be doing Insights & Analysis 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. Modeling & Agentic AI Systems 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. Experimentation 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. Value Measurement 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. Evaluations (Evals) for Agentic AI 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., This position may be subject to Lincoln's Political Contribution Policy. An offer of employment may be contingent upon disclosing to Lincoln the details of certain political contributions. Lincoln may decline to extend an offer or terminate employment for this role if it determines political contributions made could have an adverse impact on Lincoln's current or future business interests, misrepresentations were made, or for failure to fully disclose applicable political contributions and or fundraising activities. Any unsolicited resumes or candidate profiles submitted through our web site or to personal e-mail accounts of employees of Lincoln Financial are considered property of Lincoln Financial and are not subject to payment of agency fees. Lincoln Financial ("Lincoln" or "the Company") is an Equal Opportunity employer and, as such, is committed in policy and practice to recruit, hire, compensate, train and promote, in all job classifications, without regard to race, color, religion, sex, age, national origin or disability. Opportunities throughout Lincoln are available to employees and applicants are evaluated on the basis of job qualifications. If you are a person with a disability that impedes your ability to express your interest for a position through our online application process, or require TTY/TDD assistance, contact us by calling (866) 922-6543. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [AIQSpecFlow: Improves and automates your agile process of specification and creation of testcases.](https://www.wearedevelopers.com/videos/100084-aiqspecflow-improves-and-automates-your-agile-process-of-specification-and-creation-of-testcases) - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [The Missing Layer Between Enterprise Data and AI Agents](https://www.wearedevelopers.com/videos/100286-the-missing-layer-between-enterprise-data-and-ai-agents) - [All your telemetry data from any source in one place](https://www.wearedevelopers.com/videos/57-all-your-telemetry-data-from-any-source-in-one-place) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)