Data Scientist (Patient Finding)
HOW MIGHT YOU DEFY IMAGINATION
Role Overview
Live | What you will do
- Support analytics and predictive modeling projects across commercial data science use cases
- Assist in data preparation, exploratory analysis, feature engineering, and performance tracking
- Contribute to development of predictive models under guidance of senior data scientists
- Support patient finding use cases across the patient journey (e.g., pre-diagnosis signals, diagnosis, treatment triggers, relapse indicators)
- Assist in generating actionable insights and summaries for business stakeholders
- Maintain and operationalize code assets, dashboards, and data pipelines in Python/SQL environments
- Follow best practices for documentation, version control, and reproducibility
- Collaborate with cross-functional teams and participate in stakeholder discussions alongside L5 leads
- Continuously learn and apply methods such as predictive modeling, experimentation, and causal inference
Thrive | What you can expect
- Exposure to end-to-end data science delivery in a commercial pharma setting
- Opportunity to work on rare disease patient finding and patient journey analytics
- Mentorship from experienced data scientists and exposure to advanced modeling techniques
- A collaborative environment focused on learning, impact, and growth
Basic Qualifications
- Bachelors or Masters in Data Science, Engineering, Statistics, or a related quantitative field
- 35 years of experience in analytics, modeling, or data science delivery
- Working knowledge of Python and SQL; experience with modern data platforms
- Experience with data analysis, visualization, and performance tracking
- Strong attention to detail and focus on high-quality, reproducible work
Preferred Qualifications
- Exposure to predictive modeling, machine learning, or experimentation frameworks
- Basic understanding of healthcare data or patient journey analytics
- Familiarity with patient finding concepts (e.g., early signals, triggers, segmentation) is a plus
- Interest in rare disease analytics and low-signal data environments
- Experience with tools such as MLflow, Git, or notebook-based development environments
- Strong problem-solving skills and ability to work with ambiguous requirements
- Demonstrated ability to collaborate effectively with technical and business teams