Staff Applied Research Scientist

GEICO GEICO · Insurance · Palo Alto, CA +1

Staff Applied Research Scientist at GEICO responsible for leading the technical architecture and delivery of AI solutions, defining vision for scalable and compliant AI/ML, driving cross-functional initiatives, and mentoring talent. Focuses on identifying high-impact opportunities, providing architectural leadership, leading end-to-end delivery, measuring ROI, integrating research, and ensuring responsible AI adoption in a regulated environment.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • Python
  • SQL
  • PyTorch
  • TensorFlow
  • Scikit-learn
  • KPI frameworks
  • experimentation
  • causal analysis
  • stakeholder management
  • written and verbal communication

Nice to have

  • recommendation system
  • survival modeling
  • GenAI
  • LLMs
  • prompt design
  • RAG
  • evaluation strategies
  • guardrails
  • red-teaming
  • human-in-the-loop workflows
  • computer science research
  • statistics research
  • machine learning research
  • operations research
  • patents
  • publications
  • open-source contributions
  • insurance operations

What the JD emphasized

  • compliant AI/ML solutions
  • responsible, compliant, and effective adoption of AI systems
  • model governance in regulated environments

Other signals

  • lead the technical architecture and delivery of AI solutions
  • define and own the vision for scalable, robust, and compliant AI/ML solutions
  • drive high-visibility, cross-functional initiatives from ideation through production
  • mentor talent while aligning AI investments with business outcomes
  • drive innovation and operational excellence that accelerates transformation and delivers measurable value across the enterprise