Senior Staff Applied Research Scientist

GEICO•Bethesda, MD

1d•$150,000 - $300,000•Onsite

About The Position

We are seeking a Senior Staff Applied Research Scientist with profound expertise to drive the direction, vision, and architectural standards for enterprise-scale AI/ML initiatives. As a self-directed technical leader within GEICO’s AI Research team, you will architect and deliver innovative AI/ML solutions that span multiple business domains, define the future of insurance operations, and accelerate transformation across the company. You will set technical strategy, mentor scientists and engineers at all levels, and partner closely with senior leadership to align AI investments with measurable business outcomes. This is a unique opportunity to shape the architectural direction of AI at GEICO, define and deliver transformative AI/ML solutions at enterprise scale, and mentor the next generation of Applied research scientists. If you are passionate about driving innovation, measurable business impact, and a culture of excellence, we encourage you to apply.

Requirements

B.S. in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field, PhD is preferred.
Minimum of 8 years of professional experience delivering applied AI/ML solutions in production, with a proven record of leading end-to-end model development and cross-functional initiatives with enterprise impact.
Deep theoretical and practical expertise in statistical learning, machine learning, deep learning, and large language models; ability to reason from first principles and select appropriate methods under real-world constraints.
Demonstrated experience establishing KPI frameworks, experimentation strategies, and causal analysis to quantify model impact and inform prioritization.
Research experience in computer science, statistics, machine learning, operations research, or related fields; record of patents, publications, or impactful open-source contributions.
Proven ability to mentor and lead senior research scientists and engineers; establish and enforce high standards for code quality, testing, documentation, and reproducibility.
Exceptional communication and stakeholder management skills; ability to influence senior executives and drive alignment across product, engineering, and business functions.
Extensive experience with Python and SQL; deep hands-on expertise with ML frameworks such as PyTorch, TensorFlow, and Scikit-learn.
Ability to solve complex technical challenges and craft long-term strategic visions that scale across teams and business areas.

Nice To Haves

Advanced modeling experience in numerical optimization, NLP, LLMs, and reinforcement learning; familiarity with evaluation strategies, guardrails, prompt design, retrieval-augmented generation (RAG), red-teaming, and human-in-the-loop workflows.
Expertise in building domain-specific benchmarks and evaluation suites (including for GenAI/LLMs and safety), and in establishing ethical research practices and data governance suited to regulated environments.
Experience with responsible AI, model governance, and risk management in regulated environments; familiarity with insurance operations is a plus.

Responsibilities

Drive the Direction and Vision of AI/ML Initiatives: Define and own the technical strategy and long-term roadmap for cross-functional AI/ML programs, ensuring alignment with organizational goals and scalable, robust, and compliant delivery.
Identify High-Impact Opportunities: Surface and shape high-value AI use cases through deep engagement with product, engineering, data, and business leaders; translate ambiguous needs into actionable ML problem statements and investment cases.
Architecture and Technical Leadership: Architect end-to-end AI/ML systems spanning data acquisition, feature engineering, model development, evaluation, deployment, and monitoring across online and batch environments. Establish standards for scalability, reliability, observability, security, compliance, and cost efficiency.
Development and Productionization: Lead the full lifecycle of model development from concept to production, including rapid prototyping, A/B testing, causal inference, and continuous improvement with clear delivery plans and milestones.
ROI Measurement and Experimentation: Establish rigorous KPI frameworks, experimentation strategies, and causal analysis to quantify business impact, prioritize investments, and guide decision-making for portfolio-level initiatives.
Innovation and Research Integration: Stay current with cutting-edge ML/AI research. Prototype and translate novel techniques into hardened, scalable solutions within GEICO’s insurance ecosystem.
Define company-level research strategy and investment priorities; cultivate external collaborations (academia/industry) and guide the IP portfolio aligned to high-impact insurance outcomes.
Cross-Functional Collaboration: Drive collaboration across Product, Engineering, Data Platform, Governance, Legal, Security, and Operations to ensure responsible, reliable, and effective AI adoption.
Mentorship and Capability Building: Mentor Applied research scientists and engineers, elevate technical standards, and cultivate a culture of scientific rigor, engineering excellence, and continuous learning.
Presentations and Executive Engagement: Communicate complex technical concepts clearly to diverse audiences. Present to senior leadership, setting context, explaining trade-offs, and building alignment and enthusiasm for AI/ML initiatives.
Evangelism and Thought Leadership: Champion the transformative role of AI/ML across GEICO by speaking at internal forums, contributing to leadership discussions, and setting high standards that influence practices beyond your immediate team.
Strategic and Cultural Leadership: Contribute to the overarching strategy, roadmap, and operating model for AI/ML at GEICO. Foster a culture of craftsmanship, innovation, accountability, and excellence.