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# Lead Data Scientist

- **Company:** Vizient, Inc.
- **Location:** Irving, TX, United States
- **Experience:** Expert
- **Salary:** $117,600.0 - $206,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Automated Storage and Retrieval Systems, Cloud Computing, Data Discovery, Distributed Data Store, R (Programming Language), Graph Database, Apache Hadoop, Python (Programming Language), MATLAB, Machine Learning, Meta-Data Management, Natural Language Processing, Performance Tuning, SAS (Software), Search Technologies, Software Engineering, SQL Databases, Unstructured Data, Enterprise Search, Feature Engineering, Retrieval-Augmented Generation, Large Language Models, Apache Spark, Deep Learning, Generative AI, Agentic-AI, Build Management, Information Technology, Low Latency, Data Pipelines, Human in the Loop, Unsupervised Learning, Databricks, Programming Languages
- **Published:** October 30, 2026
- **Apply:** https://www.adzuna.com/details/5850516775

## About the Role

* Relevant degree preferred. Advanced degree in applied mathematics, statistics, computer science, econometrics, or a related field is a plus.
 * 7 or more years of relevant experience required.
 * Demonstrated expertise in statistical modeling, machine learning, natural language processing, generative AI, and advanced data science methodologies.
 * Experience with agentic AI concepts and environments, including LLM-based workflows, tool orchestration, retrieval-augmented generation, autonomous workflows, or human-in-the-loop patterns.
 * Expertise in programming languages such as Python, R, SQL, SAS, Java, MATLAB, or similar tools.
 * Experience with cloud-based and distributed data platforms and frameworks such as Databricks, Apache Spark, Hadoop, or similar technologies.
 * Experience with vector databases, semantic search, knowledge graphs, metadata management, or enterprise search technologies.
 * Experience deploying and maintaining machine learning and AI solutions in production environments using strong software engineering and development practices.
 * Knowledge of responsible AI, model governance, explainability, bias assessment, validation methodologies, and human oversight practices.
 * Strong analytical, problem-solving, communication, and presentation skills with the ability to translate complex technical concepts for diverse audiences.
 * Experience leading complex analytics initiatives, making technical decisions, and mentoring or providing technical guidance to data scientists.
 * Experience working with healthcare data, including clinical, claims, operational, or financial datasets preferred.

## Description

* Lead the design, development, and implementation of advanced machine learning, generative AI, agentic AI, natural language processing, statistical modeling, and unsupervised learning solutions across structured and unstructured data.
 * Design agentic AI workflows using large language models, retrieval systems, structured data, APIs, tools, and business rules, including orchestration, routing, escalation, and human approval patterns.
 * Build and deploy scalable, automated data science products and reusable analytic assets across multiple projects, domains, and client use cases.
 * Apply deep expertise across end-to-end data science workflows, including data discovery, preparation, feature engineering, modeling, validation, and insight delivery.
 * Design and optimize retrieval-augmented generation solutions and evaluation frameworks to assess AI performance, accuracy, reliability, safety, latency, cost, and consistency.
 * Make key analytical and architectural decisions, establish technical standards, and maintain ownership of solution design and outcomes aligned with strategic business objectives.
 * Lead code development and reviews, establish best practices, and mentor data scientists in advanced modeling, software development, and responsible AI.
 * Partner with engineering and platform teams to deploy, monitor, and manage the lifecycle of production machine learning, generative AI, and agentic AI solutions.
 * Implement responsible AI and model governance practices, including documentation, explainability, bias assessment, validation, risk management, and human-in-the-loop controls.
 * Translate complex analytical findings into actionable insights and communicate results effectively to technical and non-technical audiences.
 * Collaborate with cross-functional teams and clients to integrate advanced analytics and AI solutions into products, workflows, and business processes.
 * Evaluate emerging AI technologies, frameworks, and analytical approaches to advance organizational capabilities and improve data science practices.