Lead Applied Scientist – Reinforcement Learning

🔥 3 minutes ago

🇺🇸 United States – Remote

💵 $129.3k - $177.8k / year

⏰ Full Time

🟠 Senior

🧬 Research Scientist

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

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Humana

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10,000+ employees

Founded 1961

🏥 Healthcare

🛡️ Insurance

⚕️ Healthcare Insurance

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, develop, and improve machine learning and reinforcement learning models for personalized recommendations, sequential decision making, and policy learning • Apply decision science, operations research, and simulation techniques to formulate complex decision problems and evaluate alternatives • Develop and improve production ML pipelines using Python, PyTorch, Databricks, PySpark, Ray RLlib, and MLflow • Design experiments, simulations, offline evaluations, and performance metrics to assess model effectiveness, policy behavior, decision quality, and business impact • Own models and decision policies throughout their lifecycle from prototyping and testing through deployment, monitoring, and continuous improvement • Partner with software engineering and platform teams to ensure production systems are scalable, reliable, and observable • Incorporate clinical eligibility, member preferences, business rules, operational capacity, and other constraints into intelligent decision systems • Provide technical direction on model architecture, algorithm selection, evaluation methods, engineering best practices, and system design • Collaborate with data scientists, engineers, and business stakeholders

🎯 Requirements

• 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 • 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 • Ability to translate complex business problems into analytical models and production solutions • Experience owning models across the full lifecycle, including prototyping, offline evaluation, simulation, testing, deployment, and production monitoring • This role is not eligible for work visa sponsorship • Work from a dedicated space without ongoing interruptions to protect member PHI/HIPAA information • Home internet service with 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: experience with 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: familiarity with decision mapping, multi-objective decision making, and decision making under uncertainty • Preferred: experience monitoring deployed models and policies • Preferred: experience 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 • Life insurance • Flexible work hours may be possible depending on business needs • Remote/hybrid home-office work arrangement • Occasional travel for training or meetings • Home office internet requirements and dedicated workspace support for protecting PHI/HIPAA information

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