Key Responsibilities
Design and build high-impact projects that address complex medical questions across therapeutic areas, applying advanced data science methods (predictive modeling, NLP, LLMs, machine learning, causal inference).
Conduct end-to-end data science projects across structured and unstructured datasets (e.g., HCP sentiment, scientific engagement data, publications, CRM/field medical data) using Python, R, SQL, Tableau, Power BI, or similar platforms.
Drive the design, application, and evaluation of test-and-learn approaches — including causal inference methods, advanced A/B testing, and pilots, with accountability for methodological rigor and actionable insights.
Partner with business leaders and stakeholders to define problem statements, set project scope, and design and conduct medical data science projects such as MSL engagement optimization, MSL note sentiment analysis using LLMs, and digital engagement impact measurement, turning complex data into insights that enable more effective field medical activities.
Design and optimize scalable data pipelines in collaboration with engineering teams, ensuring reproducibility, robustness, and compliance in deployment workflows.
Translate complex model outputs into clear, actionable insights and recommendations and deliver compelling presentations, reports, and visualizations to medical and cross-functional leadership teams.
Serve as a technical SME (subject matter expert) in data science, providing thought influencing designing and methodology choices across the MDnA team.
Passion to continuously learn, evaluate and introduce emerging technologies (e.g., Generative AI, LLMs, advanced A/B testing frameworks) and act as a motivational peer to strengthen Amgen’s data science toolbox.
Maintain best practices in reproducibility, documentation, version control, and model governance across teams.
Basic Qualifications
PhD in Computer Science, Engineering, Statistics, or a related quantitative field and 2+ year(s) of experience in data analytics or data science; OR,
Master’s degree in Computer Science, Engineering, Statistics, or a related quantitative field and 5+ years of experience in data analytics or data science; OR,
Bachelor’s degree in Computer Science, Engineering, Statistics, or a related quantitative field and 7+ years of experience in data analytics or data science.
Experience in data science, statistics, machine learning, or advanced analytics, with hands-on application of statistical techniques such as hypothesis testing, regression analysis, clustering, and classification.
Demonstrated hands-on expertise in data science, statistical modeling, machine learning, advanced predictive modeling, NLP, or related fields, with successful delivery of end-to-end solutions.
Advanced programming in SQL and Python (R a plus); comfort working in Databricks or similar environments.
Strong storytelling and presentation skills; ability to translate data into clear, compelling insights.
Preferred Qualifications
Life sciences or pharma experience, with proven application of data science.
Deep understanding of machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
Practical experience with LLMs, RAG architectures, or advanced NLP applications is a strong plus.
Familiarity with MLflow, AWS, Git, and DevOps-style model deployment workflows.
Track record of peer mentorship and leadership within technical teams.
Soft Skills
Curiosity, adaptability, and a passion for continuous learning.
Motivational influence: ability to inspire and elevate the performance of peers.
Strong problem-solving and critical thinking.
Effective communication, both written and verbal.
Collaborative mindset and ability to work in a matrixed environment.
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