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# Data Scientist - AI & Agentic Solutions

- **Company:** Aunalytics, Inc.
- **Location:** South Bend, IN, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Governance, Python (Programming Language), Machine Learning, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Scikit Learn, Information Technology
- **Published:** July 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=6dd8d5e05cd18b61

## About the Role

* A PhD in a quantitative field (Computer Science, Statistics, Data Science, Engineering, or similar) - preferred; OR a Master's degree in a related field plus 5+ years of applied data science experience
 * Strong applied machine learning skills and fluency in Python and common data science / ML libraries
 * Hands-on experience with LLMs and agentic / generative AI - building real applications with techniques like RAG, prompt engineering, agent frameworks, and orchestration
 * A track record of taking problems from ambiguity to deployed solution, not just prototypes
 * Excellent communication and stakeholder skills - you're comfortable advising and influencing senior leaders
 * Comfort juggling multiple concurrent engagements and shifting context between clients

Nice to Have

 * Prior consulting or client-facing experience.
 * Working knowledge of data governance and compliance frameworks (SOC 2, PCI, GLBA, HIPAA)

## Description

* Partner with client leadership teams to identify where AI and AI agents can grow revenue, automate work, and improve customer experience
 * Make data AI-ready - integrate and cleanse disparate data into a foundation that models and agents can use
 * Design, build, and deploy machine learning, generative AI, and agentic workflows (from customer intelligence and lead prioritization to automating manual processes)
 * Build proofs-of-concept and production-ready solutions on the Aunalytics data platform and cloud, integrated with client systems
 * Advise on AI strategy - feasibility, risk, sequencing, and expected ROI - in language leadership can act on.
 * Define success metrics and measure impact, then iterate based on real-world results
 * Communicate clearly to both technical and non-technical audiences, from analysts to the C-suite
 * Handle regulated data responsibly, in line with client compliance requirements (SOC 2, PCI, GLBA, and HIPAA where applicable)
 * Stay current on the fast-moving AI, LLM, and agent landscape, and bring the best of it to engagements


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