Decision Intelligence Engineer – Next Best Action

🕒 May 28

🇺🇸 United States – Remote

💵 $129.3k - $177.8k / year

⏰ Full Time

🟠 Senior

🔴 Lead

👷🏻‍♀️ Engineer

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Humana

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1961

⚕️ Healthcare Insurance

Healthcare Insurance

Humana is a healthcare company dedicated to making a positive impact on the health of individuals, communities, and the healthcare system as a whole. With a focus on putting health first, Humana serves a diverse range of populations, including seniors and the military, providing Medicare Advantage HMO, PPO, and PFFS plans. Humana is committed to fostering a culture of belonging and mutual respect, offering competitive and flexible benefits to ensure the financial security of its employees and their families. The company prides itself on creating an inclusive workplace where everyone has the opportunity to succeed.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ Benefits

• 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