Data Scientist

Medical Data Scientist will play a vital role in supporting Global Medical Affairs by contributing to the design and deployment of 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 analyze and translate structured and unstructured data into actionable intelligence to inform medical strategy and improve patient outcomes. This is a hands-on, technical role that requires strong analytical ability and problem-solving skills. The ideal candidate is curious, adaptable, and eager to expand their technical expertise, including learning and applying emerging methods such as LLMs and Generative AI.

Key Responsibilities

• Support the application of data science methods including advanced statistical techniques, predictive modeling, and machine learning to design, develop, and deploy solutions that address complex medical questions across therapeutic areas.
• Support and execute components of 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.
• Assist in designing and evaluating experimentation and pilots (e.g., digital channel optimization or field medical targeting) to test innovative approaches, with a focus on scaling successful solutions.
• Work in technical teams and contribute to medical data science projects such as Medical Science Liaison (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.
• Support building and optimizing scalable data pipelines for ingestion, cleansing, transformation, model training, automation, and deployment in partnership with data engineering and platform teams.
• Collaborate with cross-functional stakeholders (Medical, Commercial, and Technology groups across Amgen India) to ensure analyses directly address key medical business questions and strategic priorities.
• Partner with data science, IT, and external vendors to support data access, integrity, and governance.
• Help to translate analytic outputs into clear, actionable insights and recommendations and deliver compelling presentations, reports, and visualizations to medical and cross-functional leadership teams.
• Stay curious continuously learn and apply emerging methods such as LLMs, Generative AI, A/B testing, and causal inference to strengthen analytical approaches.
• Support documentation, version control, and deployment practices using standardized platforms and tools.

Basic Qualifications:

• PhD in Computer Science, Engineering, Statistics, or a related quantitative field and 1+ year(s) of experience in data analytics or data science; OR,

Masters degree in Computer Science, Engineering, Statistics, or a related quantitative field and 3+ years of experience in data analytics or data science; OR,

Bachelors degree in Computer Science, Engineering, Statistics, or a related quantitative field and 5+ 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.
• 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.
• Working knowledge of SQL and Python (R a plus); comfort working in Databricks or similar environments.
• Passion for applying technical skills to solve complex business problems and drive data-driven transformation

Preferred Qualifications:

• Experience in life sciences, pharma, or regulated health analytics environments.
• Familiarity with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
• Exposure to RAG/LLM, NLP, or Generative AI applications is a strong plus.
• Familiarity with MLflow, AWS, Git, and DevOps-style model deployment workflows.
• Experience in preparing executive communication to include written and oral presentations.

Soft Skills:

• Strong storytelling and presentation skills; ability to translate data into clear, compelling insights.
• Curiosity, adaptability, and a passion for continuous learning.
• High degree of initiative and self-motivation
• Strong problem-solving and critical thinking.
• Effective communication, both written and verbal.
• Collaborative mindset and ability to work in a matrixed environment.