Data Scientist – Personalization, Intelligence

• Own the team’s semantic and intelligence architecture for member, customer, and provider profile data
• Provide system-wide design guidance and ensure robust data contracts across platform teams
• Use AI to explore and validate architectural patterns and decisions
• Design AI-augmented workflows such as automated code generation and incident analysis
• Enable personalized decisioning systems, agentic workflows, and real-time inference serving on AWS
• Establish guardrails for safe, compliant, and correct LLM-based AI usage
• Influence large cross-team projects and remove execution barriers
• Create strategies and objectives for the core intelligence and recommendation engine
• Build relationships with cross-functional teams and business stakeholders and make short- and long-term tradeoffs
• Mentor and develop data scientists and engineers across the organization on DS/ML best practices
• Participate in an on-call rotation and improve preparation and planning to reduce production incidents
• Lead large-scale initiatives to reduce technical debt, improve architecture, and meet latency and scalability targets for production AI systems

• 8+ years of experience building production ML/AI systems, inference microservices, and real-time data pipelines
• Hands-on experience with Snowflake and Open Search for profile data/vector search
• Experience with LLM orchestration, including Lang Graph and Lang Smith
• Proven Staff-level track record operating effectively in highly ambiguous territories, investigating emerging technologies, and aligning diverse stakeholders around a clear technical strategy
• Demonstrated ability to mentor and develop engineers across the organization
• Ability to cover broad technology areas while diving deep into architectural complexity and scalability for data flows, model pipelines, inference endpoints, and endpoint management
• Track record of technical leadership, creating clarity from ambiguity, defining strategy from vision, and driving organization-wide architectural improvements
• Advanced degree (MS/PhD) in Computer Science, Machine Learning, or a related field

Expertise in building production ML/AI systems and real-time data pipelines, with a strong focus on architectural complexity and scalability. Proven ability to mentor teams and influence cross-functional projects while ensuring compliance and safety in AI usage.

• Production ML/AI Systems Development
• Real-Time Data Pipelines
• LLM Orchestration
• Technical Leadership
• Mentoring Data Scientists and Engineers

• Machine Learning
• Artificial Intelligence
• Data Pipeline Development
• Architectural Design
• Inference Microservices
• Automated Code Generation
• Incident Analysis
• Scalability Optimization
• Data Contracts
• Technical Debt Reduction

• Cross-Functional Collaboration
• Stakeholder Alignment
• Strategic Thinking
• Problem Solving
• Mentorship

• Advanced Degree in Computer Science
• Advanced Degree in Machine Learning

• AI-Augmented Workflows
• Real-Time Inference Serving
• Data Science Best Practices
• Profile Data Management
• Recommendation Engine

• AWS
• Snowflake
• Open Search
• Lang Graph
• Lang Smith