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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Applied Scientist, NBA - Reinforcement Learning - **Company:** Humana Inc. - **Location:** Montgomery, AL, United States (Remote available) - **Experience:** Expert - **Salary:** $129,300.0 - $177,800.0 - **Contract:** Permanent contract - **Skills:** Automation of Tests, Big Data, Software Quality, Distributed Computing Environment, Internet Services, Python (Programming Language), Machine Learning, Software Architecture, Recommender Systems, Redis, Tensorflow, Azure Machine Learning, Software Deployment, Software Engineering, Reinforcement Learning, Pytorch, Model Validation, Vue.js, Event Driven Architecture, Pyspark, Information Technology, Apache Kafka, Machine Learning Operations, Markov, Workday, Databricks - **Published:** October 31, 2026 - **Apply:** https://dejobs.org/x/x/188227C9635C4735933FF000E8A41734/job/ ## About the Role * Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, Operations Research, or a related quantitative field. * 6+ years of professional experience in machine learning, applied science, decision science, or related engineering disciplines. * Strong experience designing, developing, testing, deploying, and monitoring machine learning solutions in production environments. * Solid understanding of reinforcement learning, sequential decision making, or other advanced decision-making techniques. * Strong Python programming skills and experience with machine learning frameworks such as PyTorch or TensorFlow. * Experience with distributed data processing and ML platforms such as Databricks, PySpark, Ray, or equivalent technologies. * Strong software engineering fundamentals, including software architecture, code quality, automated testing, and scalable system design. * Demonstrated ability to translate complex business problems into analytical models and production solutions. * Demonstrated experience owning models across the full lifecycle, including prototyping, offline evaluation, simulation, testing, deployment, and production monitoring., * Experience with reinforcement learning algorithms, contextual bandits, Markov Decision Processes, or offline policy evaluation. * Experience with operations research, simulation, or decision analysis. * Familiarity with MLflow, Ray RLlib, Databricks, and distributed model training. * Experience developing recommendation systems, personalization platforms, or Next Best Action solutions. * Experience with event-driven architectures, Kafka, Redis, or rule-based decision engines. * Familiarity with decision mapping, multi-objective decision making, and decision making under uncertainty. * Experience monitoring deployed models and policies for changes in performance, data distributions, and decision behavior. * Experience within healthcare, insurance, or other regulated industries. ## Description Humana is seeking a Lead Applied Scientist to design, develop, and scale intelligent decision systems powering our Next Best Action (NBA) platform. Our platform combines machine learning, reinforcement learning, and decision intelligence to deliver personalized recommendations that improve healthcare experiences and outcomes for millions of members. We are looking for a hands-on technical leader who can translate complex business problems into scalable, production-ready decision systems. The ideal candidate combines strong machine learning expertise with an understanding of decision science and software engineering. This role goes beyond building predictive models. You will have end-to-end ownership of models and decision policies, from initial prototyping and experimentation through offline evaluation, simulation, testing, production deployment, and ongoing monitoring. You will help define how intelligent systems make decisions, evaluate their behavior under real-world constraints and uncertainty, and continuously improve their performance based on production outcomes. Key Responsibilities Machine Learning and Reinforcement Learning Design, develop, and improve machine learning and reinforcement learning models for personalized recommendations, sequential decision making, and policy learning. Decision Intelligence and Simulation Apply decision science, operations research, and simulation techniques to formulate complex decision problems, evaluate alternatives, and improve outcomes under business and operational constraints. Scalable ML Engineering Develop and improve production ML pipelines using Python, PyTorch, Databricks, PySpark, Ray RLlib, and MLflow, supporting large-scale data processing, model training, and inference. Model Evaluation and Experimentation Design experiments, simulations, offline evaluations, and performance metrics to assess model effectiveness, policy behavior, decision quality, and business impact before and after deployment. Production Deployment and Monitoring Own models and decision policies throughout their lifecycle, from prototyping and testing through production deployment, monitoring, and continuous improvement. Partner with software engineering and platform teams to ensure production systems are scalable, reliable, and observable. Constraint-Aware Decision Making Incorporate clinical eligibility, member preferences, business rules, operational capacity, and other constraints into intelligent decision systems. Technical Leadership Provide technical direction on model architecture, algorithm selection, evaluation methods, engineering best practices, and system design while collaborating with data scientists, engineers, and business stakeholders., Work Style: Remote/Hybrid - Preferably Boston, MA. Occasional travel to Humana's offices for training or meetings may be required. Work Hours : Typical business hours are Monday-Friday, 8 hours/day, 5 days/week-- some flexibility might be possible, depending on business needs. Very minimal travel might be required for training, meetings, and/or conferences Interview Format As part of our hiring process, we will be using on-demand technology provided by Hire Vue, a third-party vendor. This technology provides our team of recruiters and hiring managers with an enhanced method for decision-making through on-demand candidate assessments. If you are selected to move forward from your application prescreen, you will receive correspondence inviting you to participate in an on-demand assessment with pre-determined questions. You should anticipate the assessment to take approximately 10-15 minutes. Your on-demand assessment will be reviewed, and you will subsequently be informed if you will be moving forward to next round of interviews. SSN Task via Workday Should you be extended a formal employment offer you will receive a request to enter your SSN into our Workday system to scan for duplicate profiles. Work at Home Requirements: To ensure Home or Hybrid Home/Office employees' ability to work effectively, the self-provided internet service of Home or Hybrid Home/Office employees must meet the following criteria: At minimum, a download speed of 25 Mbps and an upload speed of 10 Mbps is required; wireless, wired cable or DSL connection is suggested. In certain roles, the minimum recommended internet speed required by Humana may not be sufficient for business needs. Humana reserves the right to require associates to upgrade their internet service if necessary. Work from a dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information. Travel: While this is a remote position, occasional travel to Humana's offices for training or meetings may be required. Scheduled Weekly Hours 40