Decision Intelligence Engineer – Next Best Action

🕒 Maio 28

🇺🇸 Estados Unidos – Remoto (EUA)

💵 $129.300 - $177.800 / ano

⏰ Tempo Integral

🟠 Sênior

🔴 Especialista

👷🏻‍♀️ Engenheiro

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Humana

SiteLinkedInTodas as Vagas

10.000+ funcionários

Fundada em 1961

⚕️ Seguro de Saúde

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 algorithms suited to long-horizon, sparse-reward sequential decision-making in healthcare. • Frame member decisioning problems as Markov Decision Processes (MDPs) or Partially Observable MDPs. • Manage exploration-exploitation tradeoffs appropriate for a production healthcare environment. • Build simulation and backtesting environments to evaluate policy or decision quality before production promotion. • Own the nightly Databricks training workflow involving feature engineering from upstream clinical and operational data sources. • Apply multi-agent decision-making concepts where member household or population-level coordination is required.

🎯 Requisitos

• 8+ years of software engineering or quantitative research experience building and operating large-scale production systems, with emphasis on data-intensive platforms, recommendation systems, optimization engines, or simulation frameworks serving millions of users. • 3+ years of hands-on experience implementing reinforcement learning, operations research methods, or simulation-driven decision systems in production. • Relevant backgrounds include policy gradient and value-based RL (PPO, A3C, DQN, CQL), stochastic dynamic programming, discrete-event simulation, or large-scale combinatorial or constrained optimization. • Deep familiarity with Markov Decision Processes, Bellman-equation-based value estimation, reward or objective shaping, exploration-exploitation tradeoffs, and constraint formulation in real-world decision systems. • Demonstrated ability to diagnose failure modes in learned or optimized policies: instability, poor credit assignment across long horizons, and distributional shift across large populations. • Proficiency in Python 3.x; experience with PyTorch or TensorFlow for policy network or learned model implementation. • Experience with Ray RLlib or equivalent distributed computation frameworks for large-scale training or optimization. • Experience with Databricks, PySpark, and Delta Lake for large-scale ML or data pipelines processing tens of millions of records. • Experience with MLflow for experiment tracking, model registry, and artifact management. • Experience with shipping systems that operate reliably under production load, not just research or prototype work.

🏖️ Benefícios

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