Senior Data Scientist

Role Description:

The Senior Medical Data Scientist will play a vital role in supporting Global Medical Affairs by designing and deploying data science solutions including advanced statistical methods, predictive modeling, and machine learning to deliver meaningful, data-driven insights that inform scientific engagement, strategic decision-making, and patient-focused strategies.

As a key member of the Medical Data & Analytics (MDnA) team, you will translate complex structured and unstructured data into actionable intelligence to inform medical strategy and improve patient outcomes. This is a hands-on, technical, and execution-oriented role that requires both analytical depth and strong problem-solving skills. The ideal candidate is an advanced problem solver who combines deep data science expertise, hands-on technical skills, and a strong ability to mentor, motivate and elevate peers. This is an opportunity to continuously expand their expertise and take a role in pushing forward innovative analytics such as large language models (LLMs) and Generative AI.

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 Amgens 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,

Masters degree in Computer Science, Engineering, Statistics, or a related quantitative field and 5+ years of experience in data analytics or data science; OR,

Bachelors 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.