Data Sciences Sr Mgr

<h2><b>Career Category</b></h2>Engineering<h2></h2><h2><b>Job Description</b></h2><h2><span>How Might You Defy Imagination?</span></h2><p><span>At Amgen, commercial data science shapes how therapies reach patients. It informs targeting, engagement strategy, resource allocation, and measurement of impact.</span></p><p><span>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.</span></p><p><span>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.</span></p><h2><span>The Role</span></h2><p><span>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.</span></p><h2><span>Key Responsibilities</span></h2><h3><span>Commercial &amp; Analytics Leadership</span></h3><ul><li><span>Define and execute the TA commercial analytics roadmap aligned to strategic priorities.</span></li><li><span>Translate complex business questions into structured analytical problem statements.</span></li><li><span>Prioritize initiatives based on impact, feasibility, and value realization.</span></li><li><span>Represent data science in TA and GCCI governance forums.</span></li><li><span>Ensure insights are decision-ready and embedded into commercial execution.</span></li></ul><h3><span>Advanced Analytics &amp; Modeling Oversight</span></h3><ul><li><span>Lead delivery of patient and HCP analytics, including: </span><ul><li><span>Journey and cohort analyses</span></li><li><span>Segmentation and targeting strategies</span></li><li><span>Engagement effectiveness and persistence insights</span></li><li><span>Trigger/alert and identification models</span></li></ul></li><li><span>Oversee predictive modeling, machine learning, and causal measurement approaches.</span></li><li><span>Ensure robust validation, lift assessment, and experimental rigor.</span></li><li><span>Challenge model assumptions and outputs to maintain scientific credibility.</span></li></ul><h3><span>Measurement &amp; Commercial Effectiveness</span></h3><ul><li><span>Establish structured measurement frameworks for campaign and engagement impact.</span></li><li><span>Guide test-and-learn approaches, including holdouts and quasi-experimental designs.</span></li><li><span>Align measurement outputs with broader commercial performance metrics and business KPIs.</span></li></ul><h3><span>Operationalization &amp; Governance</span></h3><ul><li><span>Partner with engineering and platform teams to deploy and monitor models.</span></li><li><span>Ensure documentation, reproducibility, and governance standards are met.</span></li><li><span>Implement monitoring strategies for model performance and lifecycle management.</span></li></ul><h3><span>Team Leadership</span></h3><ul><li><span>Lead and mentor a team of data scientists aligned to the TA.</span></li><li><span>Set standards for analytical rigor, business relevance, and delivery excellence.</span></li><li><span>Build a culture of accountability, ownership, and continuous improvement.</span></li></ul><h2><span>What Success Looks Like</span></h2><ul><li><span>A clear, impact-driven analytics roadmap tied to commercial outcomes.</span></li><li><span>Analytics integrated into targeting, engagement, and investment decisions.</span></li><li><span>Strong trust with GCCI and TA leadership.</span></li><li><span>A high-performing data science team delivering consistent business value.</span></li><li><span>Measurable improvement in commercial effectiveness and ROI.</span></li></ul><h2><span>Basic Qualifications</span></h2><ul><li><span>10–15 years of experience in data science, advanced analytics, or commercial analytics leadership.</span></li><li><span>Demonstrated experience driving analytics delivery in pharmaceutical, healthcare, or other commercial industries (e.g., retail, consumer, financial services, tech).</span></li><li><span>Strong experience in predictive modeling, machine learning, and/or causal inference (hands-on or through technical oversight).</span></li><li><span>Experience translating complex analytical work into actionable business decisions.</span></li><li><span>Proven experience leading analytics teams and managing stakeholder expectations.</span></li><li><span>Proficiency in Python and SQL; experience with modern data platforms (e.g., Databricks, cloud-based environments) is preferred.</span></li></ul><h2><span>Preferred Qualifications</span></h2><ul><li><span>Experience working with healthcare or real-world longitudinal datasets (claims, EMR, specialty pharmacy).</span></li><li><span>Exposure to experimental design, uplift modeling, or structured test-and-learn frameworks.</span></li><li><span>Experience operationalizing models into production environments.</span></li><li><span>Strong executive communication and stakeholder influence skills.</span></li><li><span>Experience owning analytics roadmaps for brands, portfolios, or business units</span></li></ul><p></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p>.