Data Scientist - Remote

Role Overview

Join Mutual of Omaha’s Data Science team bridging traditional statistical modeling with advanced AI orchestration. You’ll partner with stakeholders to design and build agentic AI systems powering analytical workflows and conversational interfaces.

What You’ll Do

• Stakeholder translation & technical spec design: Convert business needs into rigorous technical specifications for AI/agent behavior.
• Advanced data manipulation: Use SQL, Python, and R to extract, profile, and prepare data from structured and unstructured sources for ML and LLM agent contexts.
• Full-stack logic integration: Ensure a seamless data flow between backend, AI agent, and UI; architect code for automated CI/CD and stable production environments.
• Build & orchestrate AI agents: Develop stateful, multi-step autonomous agents (e.g., LangGraph) for intelligent chatbots and complex business logic.
• Design production-first AI systems: Ensure Python-based agents are modular, secure, and leverage AWS Cloud.
• Observability & evaluation: Create evaluation frameworks and metrics to monitor accuracy, reliability, and cost-effectiveness for both AI agents and statistical models.
• R&D / iterative prototyping: Experiment with agent behaviors, orchestration patterns, and stateful workflows before scaling to production.

What You’ll Bring

• DS core: Graduate degree (Math/Stats/CS/BI/etc.) and 2+ years in an analytically driven role.
• Agentic expertise: 2+ years building AI agents and complex systems using LangGraph (or similar orchestration frameworks).
• Full-stack awareness: Understand end-to-end lifecycle from backend data to app logic and conversational UI.
• Required skills: Strong Python and SQL; familiarity with R.
• AWS fluency: Experience with AWS LLM ecosystem components such as Bedrock and SageMaker, plus working knowledge of AWS Lambda.
• Statistical rigor: Background in ML/statistics with ability to validate outputs against business benchmarks.
• Production perspective: Experience designing for reliability, security, and scalability.

Preferred

• PhD in an analytical field (AI, math, stats, physics, economics, data science, computer engineering/science, operations research, actuarial science).
• Strong foundation in statistical modeling/econometrics/predictive analytics, ideally with Bayesian methods, causal inference, time series, ML algorithms, Marketing Mix Models (MMM), and Customer Lifetime Value (CLTV).