Applied Research Scientist - Fraud Detection
Listed on 2026-01-12
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IT/Tech
AI Engineer, Machine Learning/ ML Engineer, Data Scientist
Job Title:
Staff Applied Research Scientist – Fraud Risk Modeling
At GEICO, we offer a rewarding career where your ambitions are met with endless possibilities.
Every day we honor our iconic brand by offering quality coverage to millions of customers and being there when they need us most. We thrive through relentless innovation to exceed our customers’ expectations while making a real impact for our company through our shared purpose.
When you join our company, we want you to feel valued, supported and proud to work here. That’s why we offer The GEICO Pledge:
Great Company, Great Culture, Great Rewards and Great Careers.
We are seeking a highly experienced and strategic Staff Applied Research Scientist to lead the technical architecture and delivery of AI solutions that span multiple functional and technical boundaries part of the AI Research team, you will define and own the vision for scalable, robust, and compliant AI/ML solutions; drive high-visibility, cross‑functional initiatives from ideation through production; and mentor talent while aligning AI investments with business outcomes.
You will drive innovation and operational excellence in fraud risk modeling by developing advanced AI solutions that enhance the detection, prevention, and investigation of fraudulent activities. Your work will focus on designing and implementing models that identify and mitigate fraud not only in insurance claims, but also across payment processing, underwriting, policy management, and customer service interactions. By leveraging state‑of‑the‑art machine learning and large language models, you will help GEICO proactively safeguard transactions and operational processes, ensuring comprehensive protection against fraud throughout the organization.
Key Responsibilities- Identify high‑impact opportunities:
Proactively surface and shape high‑value AI/ML initiatives by engaging with product, engineering, and operations to align technical roadmaps with strategic business goals. - Architecture & technical direction:
Provide architectural leadership for AI/ML solutions impacting multiple stakeholders. Establish standards for scalability, reliability, observability, compliance, and cost efficiency across online and batch systems. - Development & productionization:
Lead end‑to‑end delivery of AI/ML solutions, including model design, data pipelines, feature stores, evaluation, deployment, A/B testing, and monitoring in real‑time and batch environments. Ensure clear plans, milestones, and on‑time delivery. - ROI measurement & experimentation:
Establish robust mechanisms to quantify business impact, including KPI definition, experimentation frameworks, and causal inference approaches to guide decision‑making and prioritize investments. - Innovation & research integration:
Stay current with cutting‑edge research in ML, GenAI, and optimization. Prototype and harden novel techniques that push the boundaries of innovation within GEICO’s insurance ecosystem. - Set technical direction for multi‑quarter research initiatives; build evaluation frameworks, ensure reproducibility/responsible AI, and drive cross‑functional adoption; shepherd patents.
- Cross‑functional collaboration:
Champion collaboration across Product, Engineering, Data Platform, Governance, Legal, and Operations to ensure responsible, compliant, and effective adoption of AI systems. - Mentorship & capability building:
Mentor junior and senior scientists, elevate technical standards (coding, testing, documentation, reproducibility), and foster a culture of scientific rigor and engineering excellence. - Communication & executive engagement:
Translate complex technical topics into clear narratives for technical and non‑technical audiences. Present to senior stakeholders, set context, explain trade‑offs, and build alignment and enthusiasm. - Lead the design and implementation of AI‑driven fraud detection and prevention solutions within the car insurance domain, leveraging both machine learning (ML) and large language models (LLMs), including agentic AI systems. Develop and deploy models that identify and mitigate fraudulent activities not only in claims but also across operational processes,…
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