Data Sciences Sr Mgr
Posted Updated
How Might You Defy Imagination
The Role
Key Responsibilities
Commercial Analytics Leadership
Advanced Analytics Modeling Oversight
Measurement Commercial Effectiveness
Operationalization Governance
Team Leadership
What Success Looks Like
Basic Qualifications
Preferred Qualifications
At Amgen, commercial data science shapes how therapies reach patients. It informs targeting, engagement strategy, resource allocation, and measurement of impact.
As a Commercial Data Science Delivery Lead, you will own the analytics agenda for a Therapeutic Area (TA) and be accountable for translating data science into measurable commercial outcomes.
This is a senior leadership role for someone who can bridge business strategy and advanced analytics ensuring the right problems are solved and insights drive action.
The Role
You will serve as the single point of accountability for commercial data science delivery within your assigned TA(s), partnering with cross-functional Global Customer Capabilities and Innovation (GCCI) teams and leading a team of data scientists to drive business impact.
Key Responsibilities
Commercial Analytics Leadership
- Define and execute the TA commercial analytics roadmap aligned to strategic priorities.
- Translate complex business questions into structured analytical problem statements.
- Prioritize initiatives based on impact, feasibility, and value realization.
- Represent data science in TA and GCCI governance forums.
- Ensure insights are decision-ready and embedded into commercial execution.
Advanced Analytics Modeling Oversight
- Lead delivery of patient and HCP analytics, including:
- Journey and cohort analyses
- Segmentation and targeting strategies
- Engagement effectiveness and persistence insights
- Trigger/alert and identification models
- Oversee predictive modeling, machine learning, and causal measurement approaches.
- Ensure robust validation, lift assessment, and experimental rigor.
- Challenge model assumptions and outputs to maintain scientific credibility.
Measurement Commercial Effectiveness
- Establish structured measurement frameworks for campaign and engagement impact.
- Guide test-and-learn approaches, including holdouts and quasi-experimental designs.
- Align measurement outputs with broader commercial performance metrics and business KPIs.
Operationalization Governance
- Partner with engineering and platform teams to deploy and monitor models.
- Ensure documentation, reproducibility, and governance standards are met.
- Implement monitoring strategies for model performance and lifecycle management.
Team Leadership
- Lead and mentor a team of data scientists aligned to the TA.
- Set standards for analytical rigor, business relevance, and delivery excellence.
- Build a culture of accountability, ownership, and continuous improvement.
What Success Looks Like
- A clear, impact-driven analytics roadmap tied to commercial outcomes.
- Analytics integrated into targeting, engagement, and investment decisions.
- Strong trust with GCCI and TA leadership.
- A high-performing data science team delivering consistent business value.
- Measurable improvement in commercial effectiveness and ROI.
Basic Qualifications
- 1015 years of experience in data science, advanced analytics, or commercial analytics leadership.
- Demonstrated experience driving analytics delivery in pharmaceutical, healthcare, or other commercial industries (e.g., retail, consumer, financial services, tech).
- Strong experience in predictive modeling, machine learning, and/or causal inference (hands-on or through technical oversight).
- Experience translating complex analytical work into actionable business decisions.
- Proven experience leading analytics teams and managing stakeholder expectations.
- Proficiency in Python and SQL; experience with modern data platforms (e.g., Databricks, cloud-based environments) is preferred.
Preferred Qualifications
- Experience working with healthcare or real-world longitudinal datasets (claims, EMR, specialty pharmacy).
- Exposure to experimental design, uplift modeling, or structured test-and-learn frameworks.
- Experience operationalizing models into production environments.
- Strong executive communication and stakeholder influence skills.
- Experience owning analytics roadmaps for brands, portfolios, or business units