Lead Decision Intelligence Engineer

🕒 Agosto 20

🇺🇸 Estados Unidos – Remoto (EUA)

💵 $129.300 - $177.800 / ano

⏰ Tempo Integral

🟠 Sênior

👷🏻‍♀️ Engenheiro

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Humana

SiteLinkedInTodas as Vagas

10.000+ funcionários

Fundada em 1961

🏥 Saúde

🛡️ Seguros

⚕️ Seguro de Saúde

Healthcare • Insurance • Healthcare Insurance

A Humana é uma empresa de saúde dedicada a gerar um impacto positivo na saúde de indivíduos, comunidades e do sistema de saúde como um todo. Com foco em colocar a saúde em primeiro lugar, a Humana atende a uma ampla e diversa gama de populações, incluindo idosos e militares, oferecendo planos Medicare Advantage (HMO, PPO e PFFS). A Humana está comprometida em promover uma cultura de pertencimento e respeito mútuo, oferecendo benefícios competitivos e flexíveis para garantir a segurança financeira de seus funcionários e de suas famílias. A empresa se orgulha de criar um ambiente de trabalho inclusivo, no qual todos têm a oportunidade de ter sucesso.

Descrição

• Design, implement, and evaluate reinforcement learning algorithms for long-horizon, sparse-reward healthcare decisioning • Define and maintain member state representations and action spaces • Apply Bellman equation, reward shaping, and constraint mapping to encode clinical eligibility, suppression rules, and program objectives • Manage exploration-exploitation tradeoffs in a production healthcare environment • Build simulation and backtesting environments using historical member journey data • Diagnose and remediate policy collapse, credit assignment errors, and distributional shift • Define reward thresholds and automated evaluation gates in the nightly Databricks training workflow • Instrument training runs with MLflow tracking hyperparameters, reward curves, action distributions, and feature importance • Own the nightly Databricks workflow, including feature engineering, state vector normalization, distributed Ray RLlib training, and batch scoring of 8 million eligible members • Collaborate with Data Engineering to ensure correctly joined inputs, accurate reward signals, and reproducible, auditable pipelines • Write production-quality PySpark feature engineering jobs and maintain data lineage through Databricks Unity Catalog • Manage model artifacts, versioning, and lifecycle in the MLflow Model Registry, including rollback capability • Apply multi-agent RL concepts where household or population-level coordination is required • Implement hard business rules as constraints within the RL objective • Collaborate with the Rules Engine team to align Drools eligibility guards and RL policy priorities • Integrate model outputs with the real-time decisioning hot path and Redis-cached recommendations • Define feedback loop contracts from Kafka through Databricks Delta Live Tables into subsequent training cycles • Document model behavior, limitations, and failure modes for clinical and compliance stakeholders • Support explainability requirements for member-facing decisions • Use AI-assisted engineering tools for scaffolding, testing, and documentation while keeping core model logic and reward design human-authored and peer-reviewed

🎯 Requisitos

• Bachelor's degree in computer science or related field • 8+ years of software engineering experience building and operating large-scale production systems, with emphasis on data-intensive platforms, recommendation systems, or optimization engines serving millions of users • 3+ years of hands-on experience implementing reinforcement learning or deep learning systems in production • Experience with policy gradient methods (PPO, A3C), value-based approaches (DQN, Q-learning), or offline RL algorithms (CQL, Decision Transformer) • Deep familiarity with the Bellman equation, reward shaping, exploration-exploitation tradeoff, and constraint mapping in real-world RL systems • Ability to diagnose policy collapse, credit assignment issues, and distributional shift across large populations • Proficiency in Python 3.x • Experience with PyTorch or TensorFlow • Experience with Ray RLlib • Experience with Databricks, PySpark, and Delta Lake for large-scale ML pipelines • Experience with MLflow for experiment tracking, model registry, and artifact management • Track record of shipping reliable ML systems under production load • This role is not eligible for work visa sponsorship • Ability to work typical business hours, Monday-Friday, 8 hours/day, 5 days/week • Minimum home internet speed of 25 Mbps download and 10 Mbps upload for home or hybrid home/office employees • Ability to work from a dedicated space without ongoing interruptions to protect member PHI/HIPAA information

🏖️ Benefícios

• Bonus incentive plan based on company and/or individual performance • Medical benefits • Dental benefits • Vision benefits • 401(k) retirement savings plan • Paid time off • Company holidays • Personal holidays • Paid parental leave • Paid caregiver leave • Short-term disability • Long-term disability • Life insurance • Home or hybrid home/office work arrangement • Dedicated workspace support for protecting PHI/HIPAA information