Principal Decision Intelligence Engineer

Humana • Boston, MA

6d • $187,900 - $258,500 • Hybrid

About The Position

The Principal Decision Intelligence Engineer is the technical lead for the NBA platform's decision intelligence capabilities. This is a principal-level full stack engineering role with end-to-end ownership of the decision system: from member feature signals through optimization and decisioning logic through production deployment. You set technical direction, guide software development teams, mentor engineers, and operate with full autonomy on complex architectural problems.

Requirements

• Bachelor's degree in computer science or relevant field
• 10+ years in software engineering with deep exposure to data-intensive applications, applied AI, or decision systems, with a track record of end-to-end software delivery
• 2-5 years of product people leadership
• Experience leading a multifunctional technical team, setting direction, removing blockers, and being accountable for results
• Strong foundation in optimization and sequential decision-making, including objective functions, constraints, tradeoffs across time, and how system design choices affect what gets selected or learned
• Experience designing reward structures, evaluation criteria, or planning frameworks that translate a business goal into something a system can optimize against
• Hands-on experience with Python and the full engineering stack, including backend services, data pipelines, and AI/ML tooling (PyTorch, MLflow, PySpark, or equivalents)
• Experience building and owning pipelines on a cloud data platform (Databricks, Snowflake, or equivalent) at scale
• Strong grounding in experimental design, including constructing clean tests, avoiding confounding, and interpreting results correctly
• Able to make independent architectural decisions on complex, ambiguous problems
• Must be passionate about contributing to an organization focused on continuously improving consumer experiences
• Strong communication skills across technical and non-technical audiences
• Fluency with AI productivity tools (Claude, GitHub Copilot, or similar)

Nice To Haves

• Master's degree
• Experience with linear programming, integer programming, or constraint-based optimization in a production setting
• Background in personalization, recommendation systems, or next-best-action platforms
• Familiarity with Kafka-based event pipelines and real-time feedback loop design
• Experience with causal inference or uplift modeling
• Background in healthcare, insurance, or other regulated industries with PHI/HIPAA constraints

Responsibilities

• End-to-end decision system delivery: Own the complete pipeline from member signals to live decisions: feature engineering, decisioning logic, optimization, scoring, deployment into NBA platform software/infrastructure, and production monitoring.
• Decision intelligence architecture: Design the system that determines what to do for each member, when, and through which channel. Translate business objectives into reward structures, constraints, and optimization formulations, and know when a rule, a model, or a solver is the right tool.
• Feature and data layer: Design and build the member feature layer on the Databricks Lakehouse (clinical, behavioral, web clickstream, socioeconomic signals) in partnership with data engineering. Define what signals matter and ensure they are available, correct, and timely at decision time.
• Planning and optimization: Lead the design of how the system plans sequences of actions across a member journey, balancing short-term engagement with long-term outcome goals. This includes reward shaping, constraint satisfaction, policy evaluation, and reasoning about delayed feedback.
• Deployment and production systems: Own how decision outputs flow into live systems: scoring pipelines, serving infrastructure, action masking, and real-time feedback loops from downstream behavioral signals.
• Experimentation and measurement: Design holdout frameworks and online evaluation strategies that isolate the effect of decisioning changes. Define success metrics and translate experiment results into system improvements and roadmap input.
• Technical team leadership: Lead a team of engineers across the full delivery lifecycle. Run design reviews, set the technical quality bar, and keep delivery moving while holding the architecture together. Guide software development teams building the services, APIs, and infrastructure that support decision intelligence algorithms in production.
• Mentorship: Develop team members through code reviews, pairing, and ongoing technical guidance.
• AI-assisted development: Champion the use of AI coding and research tools (Claude, Copilot, etc.) across the team and model what high-leverage AI-augmented engineering looks like in practice.

Benefits

• medical
• dental
• vision
• 401(k) retirement savings plan
• time off (including paid time off, company and personal holidays, paid parental and caregiver leave)
• short-term and long-term disability
• life insurance

What This Job Offers

• Job Type: Full-time
• Career Level: Principal