Lead Data Scientist

Vizient•Irving, TX

About The Position

In this role, you will develop automated analytics products and design, implement, and deploy advanced data science solutions across multiple domains to address complex business and client challenges. You will lead predictive analytics, machine learning, generative AI, and agentic AI approaches, translating sophisticated analysis into actionable insights that improve clinical, operational, and economic outcomes. You will guide data scientists, influence technical strategy, and ensure high-quality, scalable, and governed solutions align with organizational objectives.

Requirements

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.

Nice To Haves

Relevant degree preferred.
Advanced degree in applied mathematics, statistics, computer science, econometrics, or a related field is a plus.
Experience working with healthcare data, including clinical, claims, operational, or financial datasets preferred.

Responsibilities

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.