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About This Role
Date: Aug 3, 2026
Primary Location: Radnor, PA, US
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Company: Lincoln Financial
Alternate Locations: Radnor, PA (Pennsylvania); Greensboro, NC (North Carolina)
Work Arrangement:
Hybrid : Employee will work 3 days a week in a Lincoln office
Relocation assistance: is not available for this opportunity.
Requisition \#: 76402
The Role at a Glance
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.
What we’re looking for
Must haves (required):
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
Applications for this position will be accepted through August 31, 2026, subject to earlier closure due to applicant volume.
What’s it like to work here?
At Lincoln Financial, we love what we do. We make meaningful contributions each and every day to empower our customers to take charge of their lives. Working alongside dedicated and talented colleagues, we build fulfilling careers and stronger communities through a company that values our unique perspectives, insights and contributions and invests in programs that empower each of us to take charge of our own future.
What’s in it for you:
Clearly defined career tracks and job levels, along with associated behaviors for each of Lincoln's core values and leadership attributes
Leadership development and virtual training opportunities
PTO/parental leave
Competitive 401K and employee benefits
Free financial counseling, health coaching and employee assistance program
Tuition assistance program
Work arrangements that work for you
Effective productivity/technology tools and training
The pay range for this position is $96,900 \- $176,200 with anticipated pay for new hires between the minimum and midpoint of the range and could vary above and below the listed range as permitted by applicable law. Pay is based on non\-discriminatory factors including but not limited to work experience, education, location, licensure requirements, proficiency and qualifications required for the role. The base pay is just one component of Lincoln’s total rewards package for employees. In addition, the role may be eligible for the Annual Incentive Program, which is discretionary and based on the performance of the company, business unit and individual. Other rewards may include long\-term incentives, sales incentives and Lincoln’s standard benefits package.
About The Company
Lincoln Financial (NYSE: LNC) helps people to confidently plan for their version of a successful future. We focus on identifying a clear path to financial security, with products including annuities, life insurance, group protection, and retirement plan services.
With our 120\-year track record of expertise and integrity, millions of customers trust our solutions and service to help put their goals in reach.
Lincoln Financial Distributors, a broker\-dealer, is the wholesale distribution organization of Lincoln Financial. Lincoln Financial is the marketing name for Lincoln Financial Corporation and its affiliates including The Lincoln National Life Insurance Company, Fort Wayne, IN, and Lincoln Life \& Annuity Company of New York, Syracuse, NY. Lincoln Financial affiliates, their distributors, and their respective employees, representatives and/or insurance agents do not provide tax, accounting or legal advice.
Lincoln is committed to creating an inclusive environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
Follow us on Facebook, X, LinkedIn, Instagram, and YouTube. For the latest company news, visit our newsroom.
Be Aware of Fraudulent Recruiting Activities
If you are interested in a career at Lincoln, we encourage you to review our current openings and apply on our website. Lincoln values the privacy and security of every applicant and urges all applicants to diligently protect their sensitive personal information from scams targeting job seekers. These scams can take many forms including fake employment applications, bogus interviews and falsified offer letters.
Lincoln will not ask applicants to provide their social security numbers, date of birth, bank account information or other sensitive information in job applications. Additionally, our recruiters do not communicate with applicants through free e\-mail accounts (Gmail, Yahoo, Hotmail) or conduct interviews utilizing video chat rooms. We will never ask applicants to provide payment during the hiring process or extend an offer without conducting a phone, live video or in\-person interview. Please contact Lincoln's fraud team at [email protected] if you encounter a recruiter or see a job opportunity that seems suspicious.
Additional Information
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\.
Salary Context
This $96K-$176K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Lincoln Financial, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($136K) sits 29% below the category median. Disclosed range: $96K to $176K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Lincoln Financial AI Hiring
Lincoln Financial has 3 open AI roles right now. They're hiring across Data Scientist, AI Product Manager, AI/ML Engineer. Based in Radnor, PA, US. Compensation range: $176K - $176K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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