Data Sciences Senior Manager

As the Therapeutic Area (TA) Decision Sciences Lead , you will be the single point of accountability for all data science and measurement work supporting your assigned TA(s). You will work closely with U.S. CD&A Decision Sciences teams to design, deliver, and operationalize models and insights that support TA-specific business needs.

Key Responsibilities

• Lead end-to-end delivery of patient analytics , including patient journey insights, cohort definitions, segmentation, adherence/persistence, and early-signal analyses.
• Drive the development of predictive models such as patient triggers, HCP alerts, identification models, and risk-based prediction frameworks.
• Oversee analytical methodologies for model measurement , including performance evaluation, causal inference, lift analysis, and test design for model validation.
• Ensure all modeling and measurement work follows Amgens standards for scientific rigor, documentation, reproducibility, and governance .
• Partner with U.S. Decision Sciences leaders to define analytical priorities, refine problem statements, and ensure TA alignment.
• Collaborate with engineering/platform teams to operationalize models , including model deployment, monitoring, drift detection, and retraining strategies.
• Review and synthesize model outputs and analytical results into structured, actionable insights for TA stakeholders.
• Mentor and guide L5/L4 data scientists supporting the TA on modeling methods, measurement frameworks, and analytic best practices.

Thrive | What you can expect

Amgen invests in your professional growth through continuous learning, leadership development, and opportunities to apply advanced analytics to meaningful patient and commercial challenges.

Basic Qualifications

• Masters or PhD in Data Science, Statistics, Computer Science, Engineering, Mathematics , or a related quantitative field.
• 12+ years of experience in data science or advanced analytics , ideally in pharmaceutical or life sciences analytics environments.
• Experience working with real-world data such as claims, EMR, specialty pharmacy, or other longitudinal datasets.
• Strong hands-on or oversight experience in predictive modeling , machine learning, and/or causal inference.
• Proficiency with Python, SQL, Databricks , and familiarity with MLflow (for model lifecycle review and guidance).
• Demonstrated ability to clearly translate complex analytical work into actionable insights for non-technical partners.
• Experience leading analytics delivery and coaching junior data scientists.

Preferred Qualifications

• Experience building alert/trigger models , patient-finding models, and next-best-action frameworks.
• Exposure to designing experiments or measurement frameworks (e.g., uplift modeling, holdouts, causal impact).
• Familiarity with cloud-based ML deployment, feature stores, or production ML practices.
• Strong communication, structured storytelling, and influence skills.