Data Scientist

Position Overview

We are looking for a Data Scientist to join our digital transformation team and help apply advanced analytics, artificial intelligence, and machine learning to real-world business and laboratory challenges.

This person will work closely with leaders across operations, finance, commercial teams, laboratory functions, and technology to identify opportunities where data and AI can improve decision-making, efficiency, customer retention, and overall business performance.

The ideal candidate can take an ambiguous business problem, determine how data can be used to address it, develop an analytical or machine learning solution, and help move that solution into a practical production environment.

What You'll Do

• Develop and implement data science and machine learning solutions addressing business and operational challenges.
• Work directly with business stakeholders to identify opportunities for predictive analytics, automation, and AI-driven improvements.
• Translate business requirements into analytical approaches, predictive models, machine learning solutions, and actionable recommendations.
• Prepare, analyze, and interpret large and complex datasets from multiple business and operational sources.
• Build, evaluate, validate, deploy, and maintain machine learning and statistical models.
• Monitor model performance and refine solutions as business requirements and underlying data change.
• Develop analytical dashboards, reporting solutions, and other tools that make complex data easier for business teams to understand and use.
• Contribute to data quality, governance, documentation, and appropriate handling of healthcare-related information.

The person in this position will have the opportunity to work on a variety of high-impact initiatives, including:

Customer Retention & Churn Analytics

• Develop models that identify customers who may be at risk of leaving.
• Help translate model outputs into actionable retention strategies.

Laboratory Logistics & Courier Analytics

• Analyze specimen collection and delivery data to improve courier performance, routing, turnaround times, and specimen stability.
• Develop predictive approaches that can identify potential delivery or collection issues.

Customer Engagement & Next-Best-Action

• Develop data-driven recommendations to help account management teams determine the most relevant actions for individual customers.
• Use customer, operational, and behavioral data to improve engagement and retention.

Workforce & Inventory Planning

• Build forecasting and predictive models to help optimize staffing, inventory levels, resource allocation, and operational planning.

Technical Environment

• Python
• SQL
• Azure, AWS, or Google Cloud
• Azure SQL or comparable relational database technologies
• Scikit-learn, TensorFlow, PyTorch, or comparable machine learning frameworks
• Statistical modeling and predictive analytics
• Machine learning model development and deployment
• MLOps, automation, or production model management
• Power BI, Tableau, or similar business intelligence platforms
• Generative AI, LLMs, or AI-enabled applications
• APIs and integration of analytical/AI solutions into enterprise applications

Qualifications

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering , or another quantitative discipline.
• Master's degree is a plus.
• Approximately 1–3 years of professional experience in data science, machine learning, artificial intelligence, advanced analytics, or a closely related field.
• Strong programming ability in Python .
• Strong SQL skills and experience working with relational databases.
• Demonstrated experience building predictive models or machine learning solutions.
• Experience taking analytical or machine learning work beyond experimentation and into a production environment.
• Familiarity with cloud-based data and machine learning environments, particularly Azure.
• Understanding of statistical modeling, predictive analytics, and model evaluation.