Senior Decision Intelligence Engineer

🕒 September 14

🇺🇸 United States – Remote

💵 $106.9k - $147k / year

⏰ Full Time

🟠 Senior

👷🏻‍♀️ Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 23%

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Humana

WebsiteLinkedInAll Job Openings

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

• Build, deploy, and operate ML and decisioning pipelines for the NBA Decision Intelligence Platform • Develop production pipelines, MLOps, feature engineering, scoring workflows, monitoring, and optimization-aware decisioning • Ensure the platform selects the right action for the right member while respecting clinical eligibility, suppression rules, channel constraints, program goals, and operational capacity • Collaborate with ML engineers, data engineers, platform engineers, product owners, and decision engine teams • Deliver reliable, scalable, and auditable production systems • Participate in on-demand HireVue assessment and subsequent interviews if selected

🎯 Requirements

• Bachelors in computer science or relevant field • 5+ years (post undergraduate level) of software engineering or quantitative research experience building and operating large-scale production systems • 2+ years (post graduate level) of software engineering or quantitative research experience building and operating large-scale production systems • 2+ years of hands-on experience implementing reinforcement learning, operations research methods, or simulation-driven decision systems in production • Experience with policy gradient and value-based RL, including PPO, A3C, DQN, and CQL • Experience with 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 • Ability to diagnose instability, poor credit assignment across long horizons, and distributional shift across large populations • Proficiency in Python 3.x • Experience with PyTorch or TensorFlow • Experience with Ray RLlib or equivalent distributed computation frameworks • Experience with Databricks, PySpark, and Delta Lake • Experience with MLflow • Experience shipping systems that operate reliably under production load • Preferred: experience with multi-agent RL frameworks such as PettingZoo • Preferred: familiarity with linear programming, mixed-integer programming, Lagrangian relaxation, or constraint programming • Preferred: experience operating decision or optimization systems in regulated domains • Preferred: experience with Gymnasium, SimPy, AnyLogic, or equivalent simulation frameworks • Preferred: familiarity with event-driven feedback loops • Preferred: OpenTelemetry instrumentation experience • Home internet with at least 25 Mbps download and 10 Mbps upload • Dedicated workspace without ongoing interruptions to protect member PHI/HIPAA information

🏖️ Benefits

• Bonus incentive plan based on company and/or individual performance • Medical benefits • Dental benefits • Vision benefits • 401(k) retirement savings plan • Paid time off • Company holidays • Personal holidays • Paid parental leave • Paid caregiver leave • Short-term disability • Long-term disability • Life insurance • Work from home or hybrid work options • Occasional travel for training, meetings, and/or conferences

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