Principal Analyst, Operations and Health Systems Research

• Design, develop, and implement complex end-to-end data pipelines, engineered features, and forecasting models for health systems utilization and clinical research
• Integrate real world data sources to build epidemiological and disease forecasting models supporting clinical trial operations decision-making
• Apply constraints-based optimization, linear and non-linear programming, simulation, and network modeling to understand participant, investigator, and sponsor behaviors affecting clinical trial outcomes
• Generate evidence and communicate results at conferences and in peer-reviewed publications
• Support the integration, design, and development of measures, models, and metrics to assess model validity and performance across the model lifecycle

• >= 5 years Pharma/Biotech/Tech/Healthcare industry experience, alternatively extensive experience in academic/clinical centers of excellence
• Expertise leveraging electronic health records and other clinical-care-derived data sources using state-of-the-art methodologies, programming languages, and tools
• Experience with machine learning, operations research, and data science problems using formal software development lifecycle approaches and best practices in a software delivery organization
• Profound experience in data science, machine learning, operations research, and data engineering programming
• Proficiency with Python/R and higher-level libraries such as numpy, pandas, scipy, stats models, scikit-learn, Google OR Tools, Tensor Flow/PyTorch, pyspark, tidyverse, sparklyr, mlr, tidy models, or similar
• Strong familiarity with simulation and forecasting models used by business stakeholders
• Experience communicating results and improving models based on end-user feedback

Demonstrates expertise in designing and implementing complex data pipelines and forecasting models for healthcare applications, utilizing advanced programming skills in Python and R. Proficient in machine learning, operations research, and data engineering, with a strong focus on model validation and performance assessment.

• Data Pipeline Development
• Machine Learning Expertise
• Python/R Proficiency
• Operations Research
• Epidemiological Modeling

• Data Science
• Forecasting Models
• Optimization Techniques
• Simulation Modeling
• Linear Programming
• Non-Linear Programming
• Network Modeling
• Model Validation
• Clinical Trial Operations
• Data Engineering

• Communication Skills
• Collaboration

• Pharma
• Biotech
• Healthcare
• Clinical Research
• Electronic Health Records
• Clinical Trial
• Data Sources
• End-User Feedback
• Software Development Lifecycle
• Clinical-Care-Derived Data

• Numpy
• Pandas
• Scipy
• Stats models
• Scikit-Learn
• Google OR Tools
• Tensor Flow
• Py Torch
• Pyspark
• Tidyverse