Data Scientist – AI Systems

Summary

Mutual of Omaha is focused on innovative solutions in the insurance industry, and they are seeking a Data Scientist to design and build AI-powered systems. The role involves collaborating with stakeholders, developing GenAI solutions, and ensuring production-grade implementations with a focus on observability and system reliability.

Responsibilities

• Stakeholder Translation & Technical Spec Design: Partner closely with business stakeholders to extract core needs and translate them into rigorous technical specifications for GenAI-powered systems and their behaviors
• Build & Orchestrate GenAI-Powered Systems: Design, develop, and refine GenAI-powered systems including AI agents, conversational interfaces, and intelligent analytical workflows. Leverage frameworks such as LangGraph and Strands where appropriate, designing stateful, multi-step workflows that navigate complex business logic. Evaluate trade-offs between architectures based on the problem at hand
• Design for Production: Design GenAI solutions with a 'production-first' mindset, ensuring Python-based systems are modular, secure, observable, and leverage AWS Cloud platform services. You write production-grade code that follows strict software development standards
• Full-Stack Logic Integration: Ensure seamless flow of data between the backend, the GenAI system, and the UI by collaborating with cloud engineers, data engineers, and UI/UX developers as needed to deliver cohesive end-to-end solutions architected for automated CI/CD pipelines and stable production environments
• Build for Observability: Develop evaluation frameworks and metrics to monitor accuracy, reliability, latency, and cost-effectiveness of GenAI-powered systems and traditional statistical models. Establish guardrails and feedback loops that catch regressions before they reach end users
• R&D with a Production Lens: Conduct focused research and prototyping to evaluate new GenAI patterns and strategies, with the clear expectation of moving from prototype to production efficiently. Know when a solution is ready to ship and iterate in production

Skills

• Graduate degree in an analytical field (Math, Stats, CS, BI, etc.) and 2+ years of experience building GenAI-powered systems such as agents, RAG pipelines, or conversational AI using frameworks like LangGraph, Strands, or similar. You've shipped models or GenAI systems to production, not just built prototypes
• Strong system design and problem-solving skills. Ability to decompose complex, under-defined problems into modular, buildable components and reason through trade-offs such as cost, latency, accuracy, and complexity
• Working knowledge of generative AI concepts and patterns including RAG architectures, data curation for knowledge bases, prompt engineering, tool-use design, memory/state management, and enterprise AI deployment pipelines
• Proficient in Python and SQL, with hands-on experience in AWS Bedrock, SageMaker, and functional understanding of AWS Lambda, AgentCore, Fargate, etc. Experience using GitHub for version control and VS Code for code development with and without Copilot assistance
• Background in ML and statistical analysis, with the ability to validate system outputs against business benchmarks and design evaluations that measure real business outcomes
• Experience designing production-grade solutions that are reliable, secure, and scalable. You know the difference between a demo and a deployed system
• PhD in AI, mathematics, statistics, physics, economics, data science, computer engineering, computer science, operations research, or actuarial science
• Experience with evaluation-driven development using LLM-as-judge, human-in-the-loop review, or structured benchmarks to measure system quality
• Familiarity with front-end technologies like Vue.js, HTML, JavaScript, or Python libraries such as Streamlit or Gradio is beneficial but not mandatory

Qualifications

Must Haves

• Graduate degree in an analytical field (Math, Stats, CS, BI, etc.) and 2+ years of experience building GenAI-powered systems such as agents, RAG pipelines, or conversational AI using frameworks like LangGraph, Strands, or similar. You've shipped models or GenAI systems to production, not just built prototypes
• Strong system design and problem-solving skills. Ability to decompose complex, under-defined problems into modular, buildable components and reason through trade-offs such as cost, latency, accuracy, and complexity
• Working knowledge of generative AI concepts and patterns including RAG architectures, data curation for knowledge bases, prompt engineering, tool-use design, memory/state management, and enterprise AI deployment pipelines
• Proficient in Python and SQL, with hands-on experience in AWS Bedrock, SageMaker, and functional understanding of AWS Lambda, AgentCore, Fargate, etc. Experience using GitHub for version control and VS Code for code development with and without Copilot assistance
• Background in ML and statistical analysis, with the ability to validate system outputs against business benchmarks and design evaluations that measure real business outcomes
• Experience designing production-grade solutions that are reliable, secure, and scalable. You know the difference between a demo and a deployed system

Nice to Haves

• PhD in AI, mathematics, statistics, physics, economics, data science, computer engineering, computer science, operations research, or actuarial science
• Experience with evaluation-driven development using LLM-as-judge, human-in-the-loop review, or structured benchmarks to measure system quality
• Familiarity with front-end technologies like Vue.js, HTML, JavaScript, or Python libraries such as Streamlit or Gradio is beneficial but not mandatory

Benefits

• Annual bonus eligibility
• 401(k) with 2% company contribution and up to 6% match
• Vacation, personal time, and paid holidays