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Job Title: Staff Applied Research Scientist – Martech AI
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 at GEICO. As 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 spearhead innovation and operational excellence in Martech AI, advancing personalized marketing solutions that enhance customer retention, engagement and conversion, and deliver measurable value across the enterprise.
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 personalized AI-driven content generation and campaign targeting solutions for marketing. Develop and deploy machine learning models that craft individualized messaging, recommend content, and optimize campaign strategies to increase customer retention, engagement and conversion. Collaborate with marketing teams to operationalize AI for hyper-personalized outreach, improving marketing ROI and user experience across digital channels.
Qualifications:
- BS in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field, PhD is preferred.
- At least 6+ years of professional experience delivering applied AI/ML solutions in production, including leading cross-functional initiatives with enterprise impact.
- Demonstrated ability to leverage LLMs and agentic AI systems to develop and deploy personalized marketing solutions, including individualized content generation, campaign targeting, and optimization of customer engagement/retention/conversion strategies.
- Strong proficiency in Python and SQL; deep experience with ML frameworks such as PyTorch, TensorFlow, and Scikit-learn
- Demonstrated experience establishing KPI frameworks, experimentation, and causal analysis to quantify model impact and inform prioritization.
- Excellent stakeholder management skills with a track record of driving alignment and adoption across product, engineering, and business teams.
- Strong written and verbal communication skills; ability to set the right context and explain complex technical topics to varied