Data Scientist (Patient Finding)

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

As we work to develop treatments that take care of others, we also work to care for our teammates’ professional and personal growth and well-being.

• 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

• Bachelor’s or Master’s in Data Science, Engineering, Statistics, or a related quantitative field
• 3–5 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

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