Job Description

Career Category

Information Systems

This Data Science Lead will report to the Sr. Data Science Manager and drive patient finding initiatives across Amgen’s RDBU portfolio. The role will work closely with the Data Science Capability Lead to leverage state-of-the-art, innovative frameworks and translate them into scalable solutions that deliver measurable business value.

What you will do

• Own patient finding delivery across brands, spanning key stages of the patient journey (pre-diagnosis, diagnosis, treatment initiation, relapse)
• Build machine learning models and predictive alerts to identify therapy-appropriate patients earlier and enable timely intervention
• Leverage aggregation of patient-level predictions to provider-level signals to improve field actionability
• Align solutions with brand strategy, field workflows, and commercial priorities
• Incorporate insights from patient support programs, hub, and benefit verification processes to enhance patient identification
• Design and execute test-and-learn frameworks (A/B testing, causal inference) to measure business impact
• Translate outputs into clear, decision-ready insights for cross-functional stakeholders
• Partner with global teams to ensure deployment, integration, and adoption of models
• Continuously improve models based on real-world performance and data constraints

Basic Qualifications

• Master’s degree in Data Science, Statistics, Computer Science, Public Health, or related field
• 5–7 years of experience in machine learning, predictive modeling, or healthcare analytics
• Strong programming skills in Python and SQL
• Experience with longitudinal healthcare data
• Understanding of patient journey analytics and experimentation methods

Preferred Qualifications

• Experience in patient finding / patient identification use cases
• Familiarity with hub services, benefit verification, and patient support programs
• Experience with early signal / pre-diagnosis modeling
• Understanding of provider-level targeting and activation
• Exposure to model lifecycle best practices (versioning, monitoring, reproducibility)
• Strong ability to translate analytics into business impact

Why this role matters

This role enables a shift from rules-based identification to ML-driven patient finding, helping identify patients earlier and drive meaningful impact on treatment outcomes.