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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Mutual of Omaha Insurance Company - **Location:** United States (Remote available) - **Experience:** Experienced - **Salary:** $100,000.0 - $130,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Business Logic, Software Quality, Databases, Database Queries, Github, Python (Programming Language), Machine Learning, Software Deployment, SQL Databases, Data Processing, Chatbots, GitHub Copilot, Large Language Models, Multi-Agent Systems, Software Application Programming, AWS Lambda, Backend, Kubernetes, Tenable Nessus - **Published:** May 15, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=9388af12e6c1cbb9 ## About the Role Do you have experience in Statistics?, The DS Core: A Graduate degree in an analytical field (Math, Stats, CS, BI etc.) and 2+ years of experience in an analytically driven role. Agentic Expertise: 2+ years of experience specifically building AI agents and designing complex systems that leverage LangGraph or similar orchestration frameworks. Full-Stack Awareness: A strong understanding of the full-stack lifecycle, including how data travels from backend databases through the application logic to a conversational UI. Language Proficiency: Strong command of Python and SQL (required). AWS Cloud Fluency: Hands-on experience building applications within the AWS LLM ecosystem (Bedrock, SageMaker) and a functional understanding of working within an AWS Lambda environment. Statistical Rigor: A strong background in machine learning and statistical analysis, with the ability to validate agent outputs against business benchmarks. Production Perspective: Experience designing solutions with a "production-grade" mindset-understanding the requirements for reliability, security, and scalability. PREFERRED: Experience with AWS Bedrock Agents and action group integration. Demonstrated ability to navigate under-defined problems and deliver robust solutions in fast-moving environments. Working knowledge of generative AI orchestration frameworks, retrieval-augmented generation (RAG) architectures, and enterprise AI deployment pipelines. We value diverse experiences, skills, and a passion for innovation. If your background aligns with this opportunity, we encourage you to apply., Here, your skills spark progress. You'll solve meaningful problems, collaborate with passionate teammates, and grow in a space where innovation is the norm-not the exception. We value diverse experience, skills, and passion for innovation. If your experience aligns with the listed requirements ## Description Join Mutual of Omaha as were looking for a Data Science team who bridges the gap between traditional statistical modeling and advanced AI orchestration. In this role, you will be partner with stakeholders serving as a key designer and builder of agentic AI systems that power analytical workflows or conversational interfaces. We are looking for a problem-solver who is as comfortable writing SQL queries as they are building autonomous agents that understand the full-stack journey., Stakeholder Translation & Technical Spec Design: Partner closely with business stakeholders to extract core needs and translate them into rigorous technical specifications for AI systems and agent behaviors. Perform Advanced Data Manipulation: Use SQL, Python to extract and profile data from structured and unstructured sources, ensuring high-quality data inputs for both machine learning models and LLM agent contexts. Full-Stack Logic Integration: Apply a "full-stack" mindset to AI development. You will use GitHub Workflows, GitHub CoPilot and AWS Bedrock to ensure a seamless flow of data between the backend, AI agents, and the UI, while ensuring your code is architected for automated CI/CD pipelines and stable production environments. Build & Orchestrate AI Agents: Develop and refine autonomous AI agents using frameworks such as LangGraph to power intelligent chatbots, designing stateful, multi-step workflows that navigate complex business logic. Design AI Systems: Design AI solutions with a "production-first" logic. While others may handle the final deployment, you are responsible for ensuring your Python-based agents are modular, and secure, leverage AWS Cloud platform. Build for Observability: Develop evaluation frameworks and metrics to monitor the accuracy, reliability, and cost-effectiveness of AI agents and traditional statistical models. R&D: Develop, experiment with, and refine autonomous AI agents using frameworks such as LangGraph, conducting iterative research and prototyping to evaluate new agent behaviors, orchestration patterns, and stateful workflows before scaling them into production-grade systems. Security: Maintain and enhance package and dependency management tools such as poetry and npm to comply with corporate development standards and resolve Critical Vulnerabilities identified by automated code quality scanning tools. ## Related Videos - [AI in Production: applied AI & enterprise use cases](https://www.wearedevelopers.com/videos/100130-ai-in-production-applied-ai-enterprise-use-cases) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Chatbots are going to destroy infrastructures and your cloud bills](https://www.wearedevelopers.com/videos/1130-chatbots-are-going-to-destroy-infrastructures-and-your-cloud-bills) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)