The Analytics Leadership Development Program (ALDP) is a leadership development program designed to grow the next generation of AI-native analytics leaders. Participants will rotate through three distinct 12–18-month assignments across analytics, data science, and AI teams. You will build AI-enabled analytics solutions, develop prototypes, and iterate on AI-powered products by applying statistical analysis, machine learning, and experimentation to real healthcare and business problems. The role requires proficiency in Python, R, and SQL to work with structured data, alongside familiarity with experimental design, hypothesis testing, and modern generative AI workflows. You will solve complex problems involving clinical analytics, affordability, and customer experience while working with sensitive healthcare data under governance standards. This role bridges the gap between academic knowledge and industrial application within a healthcare context.
Build AI-enabled analytics solutions using statistical analysis, machine learning, and experimentation to solve healthcare problems.
Leverage AI-assisted tools to accelerate problem-solving, analytics, and development while maintaining strong analytical judgment.
Develop and iterate AI-powered products end-to-end by identifying high-value problems and building prototypes.
Translate analytics and prototypes into adopted solutions by partnering with cross-functional teams to frame problems and design solutions.
Communicate complex insights clearly to diverse audiences to ensure the adoption of new tools and solutions.
Develop responsible analytics by working with sensitive healthcare data under strict governance standards.
Rotate through technical assignments in data science, AI product, MLOps, and clinical analytics teams.
What we're looking for
Master’s degree in a quantitative field such as Data Science, Analytics, Statistics, Mathematics, Computer Science, Machine Learning, or a closely related discipline.
Hands-on experience applying statistical or machine learning methods through coursework, research, capstone projects, internships, or professional roles.
Proficiency in Python (preferred) or R, and SQL for working with structured data.
Familiarity with experimental design and analytical evaluation including hypothesis testing, A/B testing, and model assessment.
Exposure to modern AI concepts and tooling, including generative AI and AI-assisted development.
Demonstrated track record using AI-assisted builder tools (preferred).
Exposure to building end-to-end solutions that move from ideas to usable output (preferred).
Generative AI
Machine Learning
Data Science
Python
SQL
AI Engineering
Data Analytics
Prototyping
Cursor
Claude
GitHub Copilot
Lovable
Product Development
Data Science
Data Engineering
Data Architecture
Business Intelligence
Machine Learning
Artificial Intelligence
Cloud Architecture
Semantic Modeling
Data Quality
Statistical Methods
Data Products
Reporting & Insights
Automation
Data Strategy
Generative AI
Agentic AI
Machine Learning
MLOps
LLMOps
Amazon Web Services
Amazon SageMaker
Amazon Bedrock
Distributed Computing
Model Serving
Observability
Production Engineering
Data Analysis
Salesforce
SQL
Python
Spark
Scala
R
Power BI
Tableau
Data Strategy
Data Governance
Big Data
Data Visualization
Machine Learning
Statistics
Data Quality Management