Sr Data Scientist

What you will do

In this vital role as part of the Data Sciences team in Global Customer Capabilities & Innovation, the Senior Data Scientist will partner with commercial insights and analytics, brand teams, sales operations, data teams, external vendor partners, media agencies, service owners, and IS partners to develop advanced analytics and data science solutions that address complex business problems across multiple therapeutic areas and commercial functions. This individual will independently lead analytical workstreams, apply deep technical expertise to large and complex healthcare datasets, develop and productionize machine learning and AI solutions, and translate sophisticated analyses into clear, actionable business recommendations. The Senior Data Scientist will also contribute technical leadership, methodological standards, and mentorship across the team.

• Leading the development and application of data science, machine learning, and artificial intelligence capabilities across Amgen's commercial organization.
• Translating complex business, medical, or scientific questions into analytical frameworks and defining appropriate solutions, success measures, delivery plans, and business impact.
• Identifying high-value business opportunities and independently recommending fit-for-purpose analytical, statistical, machine learning, or AI approaches.
• Designing, building, validating, and deploying high-performance algorithms, predictive models, prototypes, and production-ready analytical solutions using Python, SQL, and PySpark, with a focus on performance, interpretability, scalability, and actionability.
• Applying advanced measurement and experimentation approaches, including test/control design, causal inference, attribution, and other methods, to quantify business and promotional impact where appropriate.
• Partnering cross-functionally with stakeholders to align on methodology, data requirements, assumptions, limitations, and interpretation of results.
• Providing technical leadership and mentorship, contributing reusable methodologies and best practices, and staying current with developments in data science, machine learning, AI, and industry analytics.
• Communicating complex analytical findings and recommendations clearly to technical, scientific, medical, commercial, and executive stakeholders through compelling storytelling and presentations.
• Ensuring analytical solutions comply with applicable regulatory, security, privacy, data governance, and documentation requirements.

Basic Qualifications

• Doctorate degree OR Master's degree and 2 years of Data Science or related quantitative experience OR Bachelor's degree and 4 years of Data Science or related quantitative experience OR Associate's degree and 8 years of Data Science or related quantitative experience OR High school diploma / GED and 10 years of Data Science or related quantitative experience

Preferred Qualifications

• Demonstrated experience independently leading end-to-end data science or advanced analytics projects and delivering measurable business value
• Strong programming skills in Python, SQL and PySpark
• Strong experience designing, evaluating, and applying supervised and unsupervised machine learning models, with familiarity with libraries and frameworks such as Pandas, scikit-learn, TensorFlow, NumPy, PyTorch, Spark, or equivalent tools
• Experience working with large, complex datasets and modern data platforms, including distributed computing, cloud environments, Databricks/Spark, and source control technologies such as Git
• Strong statistical, analytical, and problem-solving skills, with the ability to select appropriate methodologies and communicate assumptions and limitations
• Ability to manage multiple complex analytical workstreams, prioritize effectively, and operate independently in an ambiguous, fast-paced environment
• Demonstrated ability to collaborate with and influence cross-functional business and technical partners and drive analytical projects from problem definition through implementation and adoption
• Strong communication and storytelling skills, with the ability to translate complex technical analyses into clear, precise, and actionable recommendations for business stakeholders
• Experience with commercial measurement approaches such as experimentation, test/control design, causal inference, attribution, marketing effectiveness, or related impact measurement methods
• Preferred experience with transformer-based modeling and/or other advanced machine learning approaches; practical experience with AI/GenAI use cases or strong knowledge of emerging AI capabilities is highly valued
• Biotech/pharmaceutical or healthcare industry experience, including familiarity with healthcare data such as claims, patient, HCP, sales, or promotional data, is a plus