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# Lead Data Scientist - Autonomous Goal Management

- **Company:** Humana Inc.
- **Location:** Phoenix, AZ, United States (Remote available)
- **Experience:** Expert
- **Salary:** $142,300.0 - $195,700.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Databases, Extract Transform Load (ETL), Data Mining, Python (Programming Language), Machine Learning, Language Modeling, Tensorflow, SQL Databases, Pytorch, Large Language Models, Information Technology, Search Engines, Machine Learning Operations, Network Server
- **Published:** August 22, 2026
- **Apply:** https://dejobs.org/x/x/0EA11114B2424DED90AA874434DBE39F/job/

## About the Role

* Proficiency in SQL, Python, and data analysis/data mining tools.
 * Experience with machine learning frameworks like Pytorch, Jax, LangChain, LangGraph or AutoGen
 * Experience with high performance, large-scale ML systems
 * Experience with language modeling with transformers
 * Experience with symbolic planning, causal reasoning, and model-based RL
 * Experience with large-scale ETL

Use your skills to make an impact, * Master's Degree and 4+ years of experience in research/ML engineering or an applied research scientist position preferably with a focus on developing production-ready AI solutions

 * 2+ years of experience leading development of AI/ML systems, * Ph.D. in Computer Science, Data Science, Machine Learning, or a related field
 * Demonstrated experience shipping autonomous, or semi-autonomous agents in production or simulation
 * Experience with LLM-based agents with scratchpad/self-reflection or hierarchical task decomposition
 * Understanding of autonomy risk mitigation strategies, including off-switch protocols, or bounded rationality

## Description

* Architect goal setting and goal decomposition mechanisms for autonomous agents operating in uncertain or open-ended environments
 * Design and implement dynamic planning systems (e.g., hierarchical planning, curriculum learning, scratchpad/self-refinement loops)
 * Collaborate on memory, tool-use, and feedback-loop designs enabling multi-step, self-directed agent behavior
 * Develop evaluation frameworks for alignment with human intent, consistency, progress against long-horizon objectives
 * Prototype agents that interface with APIs, MCP servers, search engines, databases, or real-world actuators to pursue goals safely and efficiency
 * Explore mechanisms to detect and mitigate goal misalignment, looping behavior, or undesirable emergent strategies
 * Work across teams to integrate goal alignment with safety, alignment, and operational reliability mechanisms


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