Lead Applied Scientist – Reinforcement Learning

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, develop, and enhance machine learning and reinforcement learning models aimed at personalized recommendations, sequential decision-making, and policy learning.

• Utilize decision science, operations research, and simulation methodologies to define complex decision challenges and assess various alternatives.

• Create and refine production ML pipelines utilizing Python, PyTorch, Databricks, PySpark, Ray RLlib, and MLflow.

• Design experiments, simulations, offline evaluations, and performance metrics to evaluate model efficacy, policy behavior, decision quality, and business impact.

• Take ownership of models and decision policies throughout their lifecycle, from prototyping and testing to deployment, monitoring, and ongoing enhancement.

• Collaborate with software engineering and platform teams to ensure that production systems are scalable, reliable, and observable.

• Integrate clinical eligibility, member preferences, business rules, operational capacity, and other constraints into intelligent decision-making systems.

• Provide technical guidance on model architecture, algorithm selection, evaluation techniques, engineering best practices, and system design.

• Work in partnership with data scientists, engineers, and business stakeholders.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, Operations Research, or a related quantitative discipline.

• 6+ years of professional experience in machine learning, applied science, decision science, or comparable engineering fields.

• Extensive experience in designing, developing, testing, deploying, and monitoring machine learning solutions within production settings.

• Strong grasp of reinforcement learning, sequential decision-making, or other advanced decision-making methodologies.

• Proficient in Python programming.

• Familiarity with machine learning frameworks such as PyTorch or TensorFlow.

• Experience with distributed data processing and ML platforms such as Databricks, PySpark, Ray, or similar technologies.

• Solid software engineering fundamentals, encompassing software architecture, code quality, automated testing, and scalable system design.

• Capability to translate intricate business issues into analytical models and production solutions.

• Experience managing models throughout their entire lifecycle, including prototyping, offline evaluation, simulation, testing, deployment, and production monitoring.

• This position is not eligible for work visa sponsorship.

• Ability to work from a dedicated space without ongoing interruptions to safeguard member PHI/HIPAA information.

• Home internet service must provide at least 25 Mbps download and 10 Mbps upload speed.

• Preferred: experience with reinforcement learning algorithms, contextual bandits, Markov Decision Processes, or offline policy evaluation.

• Preferred: background in operations research, simulation, or decision analysis.

• Preferred: familiarity with MLflow, Ray RLlib, Databricks, and distributed model training.

• Preferred: experience developing recommendation systems, personalization platforms, or Next Best Action solutions.

• Preferred: experience with event-driven architectures, Kafka, Redis, or rule-based decision engines.

• Preferred: knowledge of decision mapping, multi-objective decision making, and decision making under uncertainty.

• Preferred: experience in monitoring deployed models and policies.

• Preferred: background in healthcare, insurance, or other regulated industries.

🏝️ Benefits

• Bonus incentive plan based on company and/or individual performance.

• Medical, dental, and vision benefits.

• 401(k) retirement savings plan.

• Paid time off.

• Company and personal holidays.

• Paid parental and caregiver leave.

• Short-term and long-term disability coverage.

• Life insurance.

• Flexible working hours may be available based on business needs.

• Remote/hybrid home-office work arrangement.

• Occasional travel for training or meetings.

• Home office internet requirements and dedicated workspace support to protect PHI/HIPAA information.