Senior Scientist – Scientific Data & ML Enablement

Amgen; Hyderabad; Hybrid; INR 4,500,000 - 7,000,000; Full time

Benefits offered by this job

Retirement and Savings Plan

Group medical, dental, and vision

Life and disability insurance

Flexible spending accounts

Job summary

Amgen in Hyderabad seeks a Senior Scientist to enable AI-driven research by designing, managing, and optimizing scientific datasets for ML applications in Large Molecule Discovery Informatics. You will create scalable data models and ML‑ready datasets, partnering with scientists and data engineers to ensure data is structured, governed, and reusable across discovery pipelines.

The role emphasizes data governance, cross‑functional collaboration, and building foundational data assets to accelerate

Qualifications

• Doctorate degree (PhD) with 5+ years of relevant experience OR Master’s with 8+ years of related experience OR Bachelor’s with 10+ years of related experience.
• Experience designing data models for scientific, analytical, or ML applications.
• Strong proficiency in SQL, Python, and modern data engineering platforms.

Responsibilities

• Design and maintain scalable data models for ML, analytics, and research workflows.
• Create and maintain ML‑ready datasets for model training, validation, and deployment.
• Collaborate with scientists to translate workflows into effective data structures and reusable datasets.
• Implement metadata, lineage, and governance practices to improve data quality and reuse.
• Develop standardized approaches for biological, assay, sequencing, and protein engineering datasets.

Skills

SQL

Python

Data engineering

Analytics

Cross-functional collaboration

Education

PhD in a relevant field

Master's degree in a related field

Bachelor's degree in a related field

Tools

Modern data platforms

Job description

Senior Scientist – Scientific Data & ML Enablement (Large Molecule Discovery Informatics)

In this vital role, you will enable AI-driven research across Large Molecule Discovery by designing, managing, and optimizing the scientific datasets that power machine learning applications. This role will focus on transforming complex biological and experimental data into reliable, reusable, and ML‑ready assets that support model development, deployment, and long‑term scalability.

Working at the intersection of data engineering, scientific informatics, and machine learning, this individual will partner with scientists, AI/ML researchers, software engineers, and data platform teams to ensure that discovery data is structured and accessible for advanced analytics and AI applications. The successful candidate will help establish scalable data models, metadata frameworks, and transformation pipelines that improve data quality, consistency, and reuse across the LMD ecosystem.

This role is ideal for someone who enjoys solving complex scientific data challenges and is passionate about building the data foundations necessary to accelerate AI‑enabled drug discovery.

Core responsibilities include:

• Design and maintain scalable data models supporting machine learning, analytics, and scientific research workflows
• Create and maintain ML‑ready datasets for model training, validation, and deployment
• Partner with scientists to translate experimental workflows into effective data structures and reusable datasets
• Implement metadata, lineage, and governance practices that improve data quality, traceability, and reuse
• Develop standardized approaches for biological, assay, sequencing, and protein engineering datasets
• Design and optimize data transformation workflows supporting machine learning and analytics use cases
• Support integration of data from multiple scientific systems and repositories
• Collaborate with scientists, bioinformaticians, engineers, and AI teams to accelerate AI‑enabled discovery research

What we expect of you

Basic Qualifications

• Doctorate degree (PhD) with 5+ years of relevant experience
• or Master’s degree and 8+ years of directly related experience
• or Bachelor’s degree and 10+ years of directly related experience

Preferred Qualifications

• Experience designing data models supporting scientific, analytical, or machine learning applications
• Strong proficiency in SQL, Python, and modern data engineering platforms
• Experience creating datasets for machine learning, predictive modeling, or advanced analytics
• Understanding of metadata, data lineage, governance, and reproducibility concepts
• Experience working with biotechnology, pharmaceutical, genomics, or life science datasets
• Familiarity with scientific data platforms and research informatics environments
• Strong analytical, communication, and cross‑functional collaboration skills

What You Can Expect Of Us

• Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental, and vision coverage, life and disability insurance, and flexible spending accounts.
• A discretionary annual bonus program, or for field sales representatives, a sales‑based incentive plan
• Stock‑based long‑term incentives
• Award‑winning time‑off plans and bi‑annual company‑wide shutdowns
• Flexible work models, including remote work arrangements, where possible

Application deadline: Amgen does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position.

Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.