AVP, Machine Learning & Modeling

Vizient•Chicago, IL

2d•Hybrid

About The Position

In this role, you will lead the design, development, and implementation of advanced analytics, statistical modeling, and artificial intelligence solutions to drive data-informed decision-making across the enterprise. Oversee teams of data scientists, modelers, and ML engineers to deliver innovative and scalable solutions that support strategic business objectives, operational excellence, and client value creation. Serve as a key thought leader, guiding the organization’s data science strategy, ensuring model governance and compliance, and advancing the use of cutting-edge machine learning methodologies.

Requirements

Bachelors or Masters or Doctorate degree in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, Economics, or a related quantitative field preferred
Expertise in machine learning, deep learning, and statistical modeling techniques (e.g., regression, time series, NLP, ensemble methods, neural networks).
Strong proficiency in Python, SQL, and with cloud-based AI/ML platforms (AWS SageMaker, Azure ML, Databricks, etc.)
Knowledge of ML Ops, model governance, and lifecycle management best practices.
Demonstrated ability to translate business problems into analytical frameworks and deliver measurable outcomes.
Excellent leadership, communication, and stakeholder management skills, with the ability to present complex findings to non-technical audiences.
Proven experience building and leading high-performing data science teams in a large, matrixed organization.
Familiarity with healthcare, financial, or operational analytics.

Responsibilities

Provide strategic direction for the organization’s machine learning and modeling initiatives.
Develop and execute a roadmap for data-driven innovation aligned with corporate goals and emerging technologies in AI, predictive analytics, and optimization.
Oversee the design, development, and validation of statistical, econometric, and machine learning models that support forecasting, risk assessment, operational efficiency, and client performance improvement.
Ensure model accuracy, interpretability, and ethical use of data.
Establish and maintain robust model governance frameworks, including documentation, version control, validation, and ongoing performance monitoring.
Partner with risk management and compliance teams to ensure adherence to regulatory and ethical standards.
Lead and mentor a multidisciplinary team of data scientists, ML engineers, and quantitative analysts.
Foster a culture of innovation, continuous learning, and collaboration while ensuring alignment with business priorities and project timelines.
Collaborate with business leaders, IT, and analytics stakeholders to identify opportunities for data science applications that drive measurable business outcomes.
Translate complex analytical results into actionable insights for executive decision-making.
Stay at the forefront of emerging AI and machine learning technologies.
Evaluate and integrate new tools, frameworks, and methodologies to enhance model performance and scalability.
Promote experimentation and best practices in applied data science.