Lead Applied Scientist, NBA – Reinforcement Learning

Humana Inc; United States; Hybrid; USD 129,000 - 178,000; Full time

Benefits offered by this job

Remote/Hybrid work options

Bonus incentive plan

Job summary

Humana Inc. is seeking a Lead Applied Scientist to design, develop, and scale intelligent decision systems powering our Next Best Action (NBA) platform.

You will own models and decision policies end-to-end, from prototyping to production monitoring, with emphasis on decision quality and business impact. You will lead ML and RL design, leverage Python stack and Databricks-based pipelines, and collaborate with data scientists, engineers, and stakeholders to deliver scalable solutions in a

Qualifications

• Bachelor's degree in a quantitative field and 6+ years of related experience.
• Strong ML engineering experience with production deployment and monitoring.
• Proficient in Python and ML frameworks (PyTorch, TensorFlow).
• Experience with reinforcement learning and sequential decision making.
• Ability to translate business problems into scalable decision systems.

Responsibilities

• Design and scale ML/RL models for personalized recommendations and policy learning.
• Apply decision science and simulation to optimize outcomes under constraints.
• Develop production ML pipelines and ensure reliability and observability.
• Lead technical direction on model architecture and evaluation methods.
• Own models across full lifecycle from prototyping to monitoring.

Skills

Machine Learning

Reinforcement Learning

Decision Science

Python

PyTorch

Databricks

PySpark

Ray RLlib

MLflow

Production systems

Education

Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, Operations Research, or related quantitative field

Tools

Databricks

PySpark

Ray RLlib

MLflow

Job description

Become a part of our caring community

Become a Part of Our Caring Community 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Additional Information

This role is not eligible for work visa sponsorship.

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

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 Pay Range

The compensation range below reflects a good faith estimate of starting base pay for full time (40 hours per week) employment at the time of posting. The pay range may be higher or lower based on geographic location and individual pay will vary based on demonstrated job related skills, knowledge, experience, education, certifications, etc. $129,300 - $177,800 per year This job is eligible for a bonus incentive plan. This incentive opportunity is based upon company and/or individual performance.

Description of Benefits

Humana, Inc. and its affiliated subsidiaries (collectively, “Humana”) offers competitive benefits that support whole-person well-being. Associate benefits are designed to encourage personal wellness and smart healthcare decisions for you and your family while also knowing your life extends outside of work. Among our benefits, Humana provides medical, dental and vision benefits, 401(k) retirement savings plan, time off (including paid time off, company and personal holidays, paid parental and caregiver leave), short-term and long-term disability, life insurance and many other opportunities.

Application Deadline: 11-03-2026

About us

About Humana: Humana Inc. (NYSE: HUM) is a leading U.S. healthcare company. Through our Humana insurance services and our CenterWell healthcare services, we make it easier for the millions of people we serve to achieve their best health – delivering the care and service they need, when they need it. These efforts are leading to a better quality of life for people with Medicare and Medicaid, families, individuals, military service personnel, and communities at large. Learn more about what we offer atHumana.comand atCenterWell.com.

Equal Opportunity Employer

It is the policy of Humana not to discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or protected veteran status. It is also the policy of Humana to take affirmative action, in compliance with Section 503 of the Rehabilitation Act and VEVRAA, to employ and to advance in employment individuals with disability or protected veteran status, and to base all employment decisions only on valid job requirements. This policy shall apply to all employment actions, including but not limited to recruitment, hiring, upgrading, promotion, transfer, demotion, layoff, recall, termination, rates of pay or other forms of compensation and selection for training, including apprenticeship, at all levels of employment.