Principal Decision Intelligence Engineer

Humana

Principal Decision Intelligence Engineer at Humana, overseeing the NBA platform's decision intelligence systems and team leadership. Driving full stack engineering with end-to-end ownership on complex architectural problems.

Posted 7/18/2026full-timeBoston • Massachusetts • 🇺🇸 United StatesLead💰 $187,900 - $258,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in end-to-end decision system delivery, including optimization, feature engineering, and deployment within a cloud data platform. Proven ability to lead technical teams, design reward structures, and apply AI/ML tools to enhance decision intelligence.

Highest-signal resume keywords

End-To-End Software DeliveryOptimization and Sequential Decision-MakingPython and Full Engineering StackCloud Data Platform ExperienceTechnical Team Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Feature EngineeringDecision Intelligence ArchitectureReward Structures DesignExperimental DesignData Pipeline Development

Soft Skills

Strong Communication SkillsMentorship and Team Development

Tools & Technologies

DatabricksPyTorchMLflowPySparkAI Productivity Tools

Certifications & Qualifications

Bachelor's Degree in Computer Science

Industry Keywords

Data-Intensive ApplicationsDecision SystemsConsumer Experience ImprovementBehavioral SignalsReal-Time Feedback Loops

Tech Stack

Tools & technologies

CloudPySparkPythonPyTorch

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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).

Benefits

Comp & perks

medical, dental and vision benefits
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
many other opportunities