[Remote] Decision Intelligence Engineer - Next Best Action

Note: The job is a remote job and is open to candidates in USA. Humana Inc. is a leading U.S. healthcare company that is seeking a skilled Decision Intelligence Engineer to enhance their Next Best Action platform. The role involves designing and evaluating decision-making algorithms, ensuring system compliance with clinical rules, and collaborating with data and platform engineers.

Responsibilities

• Design, implement, and evaluate algorithms suited to long-horizon, sparse-reward sequential decision-making in healthcare. These algorithms include reinforcement learning methods, such as PPO, A3C, DQN, CQL, and Decision Transformer, as well as dynamic programming formulations and constrained optimization approaches
• Frame member decisioning problems as Markov Decision Processes (MDPs) or Partially Observable MDPs, defining state representations, action spaces, transition dynamics, and reward structures that encode clinical and program-specific goals
• Apply Bellman-equation-based value estimation, reward shaping, and constraint formulations to encode clinical eligibility, suppression rules, and program-specific objectives directly into the learning or optimization objective
• Manage exploration-exploitation tradeoffs (or equivalent uncertainty-handling in simulation and stochastic optimization) appropriate for a production healthcare environment where suboptimal actions have member impact
• Model member journey dynamics using tools from stochastic processes, simulation, or probabilistic graphical models to inform policy design and evaluate
• Build simulation and backtesting environments, including discrete-event simulation and Monte Carlo methods, to evaluate policy or decision quality before production promotion using historical member journey data
• Diagnose failure modes specific to learned or optimized policies. These include policy collapse, credit assignment errors across long member journeys, distributional shift between training and serving populations, and constraint violations under out-of-distribution inputs. Remediate these failure modes
• Define performance threshold criteria and automated evaluation gates within the nightly Databricks training workflow; block promotion of underperforming policies to MLflow production
• Instrument training and optimization runs with MLflow tracking covering hyperparameters, objective curves, action distributions, and feature importance for every training cycle
• Own the nightly Databricks training workflow. This workflow involves feature engineering from upstream clinical and operational data sources, and state vector normalization. Additionally, it includes distributed training by Ray RLlib (or equivalent optimization solvers), and batch scoring of all eligible members
• Collaborate with the Data Engineering team to ensure the Data Engineering team correctly joins training inputs, computes reward signals from disposition outcomes, and makes the feature pipeline reproducible and auditable
• Write production-quality PySpark feature engineering jobs; maintain data lineage through Databricks Unity Catalog
• Manage model artifacts, versioning, and lifecycle in the MLflow Model Registry; ensure rollback capability is maintained at all times
• Apply multi-agent decision-making concepts (MARL via PettingZoo, or game-theoretic or cooperative optimization approaches) where member household or population-level coordination is required
• Implement constraint handling to enforce hard business rules directly within the optimization objective. These rules include member caps, cooldown periods, and clinical eligibility. To achieve this, use constrained MDP formulations, Lagrangian relaxation, or mixed-integer programming as appropriate, rather than relying on downstream filters
• Collaborate with rules engine stakeholders to ensure eligibility guards and policy priorities are correctly aligned and do not conflict
• Partner with decision engine and rules engine teams to ensure that you integrate model outputs cleanly with the real-time decisioning hot path and that you correctly structure and interpret scored recommendations
• Collaborate with platform architects to define feedback loop contracts: how disposition outcomes flow back through the data pipeline into the next training cycle
• Document model behavior, known 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; ensure all core model logic and objective design remain human-authored and subject to rigorous peer review

Skills

• 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
• Experience with multi-agent RL frameworks (PettingZoo or equivalent) or multi-agent simulation and coordination methods
• Familiarity with operations research methods applicable to constrained sequential decisioning: linear programming, mixed-integer programming, Lagrangian relaxation, or constraint programming
• Experience operating decision or optimization systems in regulated domains (healthcare, finance, or insurance) where member safety, auditability, and explainability are requirements
• Experience building simulation environments using Gymnasium, SimPy, AnyLogic, or equivalent frameworks for policy evaluation and backtesting
• Familiarity with event-driven feedback loops and how disposition signals feed retraining or re-optimization pipelines
• OpenTelemetry instrumentation experience for ML or optimization pipeline observability

Benefits

• This job is eligible for a bonus incentive plan. This incentive opportunity is based upon company and/or individual performance.
• Humana, Inc. and its affiliated subsidiaries (collectively, 7Humana8) offers competitive benefits that support whole-person well-being.
• 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
• Employees who live and work from Home in the state of California, Illinois, Montana, or South Dakota will be provided a bi-weekly payment for their internet expense.
• Humana will provide Home or Hybrid Home/Office employees with telephone equipment appropriate to meet the business requirements for their position/[job.Work](http://job.Work) from a dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information.

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