Lead Data Scientist – Commercial Intelligence & ML Operations

Description

The company is a Fortune 10 healthcare firm whose insights, products and services aim to make quality care more accessible and affordable.

This is a Lead Data Scientist role in Pricing Intelligence and Machine Learning Operations, leading the build of advanced analytical and ML solutions that improve pricing strategy and run reliably and at scale in operational systems.

Medical-surgical distribution unit (MMS), a publicly reported segment of the company and expected to become Wellverse in early 2027, supplies medical-surgical products, pharmaceuticals and diagnostic equipment to non-acute care settings such as physician offices, surgery centers, long-term care, laboratories, home health and hospice, health systems, government facilities and online retailers.

The unit runs about 8,000 employees, 15 distribution centers and roughly 900 delivery vehicles, works with more than 2,200 manufacturers and serves over 200,000 customer accounts across the U.S. Its catalog holds more than 270,000 branded and private-label SKUs, from bandages to specialty pharmaceuticals and COVID-19 tests.

Responsibilities

• Build and upkeep predictive and machine learning models, and keep improving them.
• Lead advanced analytics work across customer, product, pricing and commercial areas.
• Watch production models for accuracy, reliability and data quality.
• Diagnose model and data problems across analytical workflows.
• Set the standards for model validation, testing, monitoring and governance.
• Turn analysis into clear recommendations for the business.
• Work with engineering teams to put models and analytical solutions into production.
• Guide and coach other data scientists.
• Help develop optimization, forecasting, recommendation and AI-driven decision tools.

Requirements

• At least 7 years of experience in data science, machine learning, advanced analytics or a closely related quantitative field.
• Strong Python and SQL skills.
• Solid grounding in statistical modeling, machine learning, regression, clustering and predictive analytics.
• Experience deploying and supporting models in production.
• Experience with large datasets and cloud-based analytics platforms.
• Good understanding of data pipelines, model inputs and outputs, and data-quality checks.
• Experience with model monitoring, lifecycle management and explainability.
• Strong problem-solving and communication skills.
• Proven ability to lead technical work and work across business and technology teams.
• A bachelor's degree in a quantitative or technical field, with substantial relevant experience, is required.
• A master's degree in data science, statistics, computer science, applied mathematics, operations research, economics, engineering or another quantitative discipline is preferred.
• A PhD in a quantitative discipline is a plus but not required.
• Preferred: experience in pricing, revenue management, forecasting, optimization or commercial analytics.
• Preferred: experience with recommendation systems, segmentation or predictive modeling.
• Preferred: experience with MLOps and production model deployment.
• Preferred: familiarity with generative AI and modern AI-assisted analytical solutions.
• Preferred: experience supporting business-critical analytical applications.
• Preferred: prior experience mentoring or leading other data scientists.
• Preferred: experience in healthcare or pharmaceuticals is a plus.

Conditions

The base pay range is $155,300 to $258,800.

Pay depends on performance, experience and skills, equity, regular job market reviews and geographic market, and follows the company's pay philosophy and applicable regulations.

An annual bonus or long-term incentive may be offered on top of base pay.

Equal opportunity employer.

Benefits

• Access to the company's Total Rewards package, with details on benefits available on the careers site.