Principal Decision Intelligence Engineer

Humana; Massachusetts; Hybrid; USD 187,900 - 258,500; Full time

Benefits offered by this job

Medical, dental, and vision benefits

401(k) retirement savings plan

Paid time off including holidays and p

Disability and life insurance

Wellness programs

Job summary

Humana is seeking a Principal Decision Intelligence Engineer to serve as the technical lead for the NBA platform’s decision intelligence capabilities. You will own the full decision system—from member signals to production deployment—driving architecture, mentorship, and high‑impact outcomes.

You will guide a multifunctional team, design optimization solutions, and ensure scalable, compliant data pipelines across the stack using Python, Databricks, and modern ML tooling.

Qualifications

• Bachelor’s degree in Computer Science or a related field.
• 10+ years of software engineering experience with data‑intensive applications, applied AI, or decision systems.
• 2–5 years of product leadership experience.
• Experience leading a multifunctional technical team, setting direction and removing blockers.
• Strong foundation in optimization and sequential decision‑making, with system design impact.
• Experience designing reward structures or planning frameworks translating business goals into objectives.
• Hands‑on experience with Python and full stack including backend services, data pipelines, and AI/ML tooling.
• Experience building pipelines on a cloud data platform at scale.
• Strong analytical and communication skills.
• Familiarity with AI productivity tools.

Responsibilities

• Own the complete pipeline from member signals to live decisions, including feature engineering, scoring, and production monitoring.
• Design the decision intelligence architecture that translates objectives into reward structures and constraints.
• Develop the member feature layer on the Databricks Lakehouse with data engineering collaboration.
• Lead planning and optimization of action sequences across member journeys.
• Own the flow of decision outputs into live systems and real‑time feedback loops.
• Design holdout frameworks and online evaluation strategies to measure impact.
• Lead and grow a multifunctional engineering team; set standards and maintain cohesion.
• Mentor engineers through code reviews and pairing; promote best practices.
• Champion AI coding and research tools to maximize productivity.

Skills

Leadership

Python

Optimization

Experiment design

Communication

Cloud platforms

AI tooling

Decision systems

Education

Bachelor’s degree in Computer Science or related field

Master’s degree (strong plus)

Tools

Databricks

Snowflake

PyTorch

MLflow

Spark

Kafka

Job description

Overview

The Principal Decision Intelligence Engineer is the technical lead for the NBA platform’s decision intelligence capabilities. This principal‑level full stack engineering role owns the entire decision system—from member feature signals through optimization, decisioning logic, and production deployment—while setting technical direction, mentoring engineers, and solving complex architectural problems with full autonomy.

Responsibilities

• Own the complete pipeline from member signals to live decisions, including feature engineering, scoring, and production monitoring.
• Design the decision intelligence architecture that determines actions for each member, translating business objectives into reward structures, constraints, and optimization formulations.
• Develop the member feature layer on the Databricks Lakehouse, collaborating with data engineering to ensure signals are accurate, timely, and available.
• Lead planning and optimization of action sequences across member journeys, balancing short‑term engagement with long‑term outcomes.
• Own the flow of decision outputs into live systems: scoring pipelines, serving infrastructure, action masking, and real‑time feedback loops.
• Design holdout frameworks and online evaluation strategies to isolate the impact of decisioning changes and define success metrics.
• Lead and grow a multifunctional engineering team, running design reviews, setting technical standards, and maintaining architectural cohesion.
• Mentor team members through code reviews, pairing, and continuous technical guidance.
• Champion the use of AI coding and research tools across the team to maximize productivity.

Qualifications

• Bachelor’s degree in computer science or a related field.
• 10+ years of software engineering experience with deep exposure to data‑intensive applications, applied AI, or decision systems.
• 2–5 years of product people leadership experience.
• 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, trade‑offs over time, and system design impacts.
• Experience designing reward structures, evaluation criteria, or planning frameworks that translate business goals into system‑optimizable objectives.
• 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.
• Capacity to make independent architectural decisions on complex, ambiguous problems.
• Passion for improving consumer experiences in an organization focused on continuous enhancement.
• Strong communication skills across technical and non‑technical audiences.
• Fluency with AI productivity tools (Claude, GitHub Copilot, or similar).

Strong Plus

• Master’s degree.
• Experience with linear programming, integer programming, or constraint‑based optimization in production.
• 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.

Work Style & Logistics

• Hybrid – Boston, MA with occasional travel to Humana’s offices for training or meetings.
• Typical business hours: Monday‑Friday, 8 hours/day, 5 days/week (some flexibility).
• Minimum internet speed requirement: 25 Mbps download × 10 Mbps upload via DSL/cable; satellite and wireless internet not allowed.
• Work‑from‑home setting must provide a dedicated space free from interruptions to protect PHI/HIPAA information.

Compensation

$187,900 – $258,500 per year. This range reflects a good‑faith estimate of starting base pay for full‑time (40 hours per week) employment at the time of posting. Salary may vary based on geographic location and individual qualifications. Eligible for a bonus incentive plan based on company and/or individual performance.

Benefits

Competitive medical, dental, and vision coverage; 401(k) retirement savings plan; paid time off (including holidays, parental and caregiver leave); short‑term and long‑term disability; life insurance; and additional wellness programs.

Equal Opportunity Employer

Humana is an equal‑opportunity employer and complies with applicable laws and regulations. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability, or protected veteran status. We also take affirmative action to employ and advance qualified individuals with disability or protected veteran status, considering only valid job requirements for all employment decisions.