Senior Scientist - Scientific Data & ML Enablement

Amgen Technology; Hyderabad; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Amgen Technology in Hyderabad, India, seeks a Senior Scientist in Scientific Data ML Enablement to design, manage, and optimize ML-ready datasets powering Large Molecule Discovery Informatics. You will build scalable data models, enable data governance, and partner with scientists and AI teams to accelerate discovery efforts.

The role focuses on transforming complex biological and experimental data into reusable assets for model development, deployment, and scalable research.

Qualifications

• PhD with 5+ years of relevant experience
• Masters with 8+ years of directly related experience
• Bachelor's with 10+ years of directly related experience
• Experience designing data models for scientific, analytical, or ML applications
• Understanding of metadata, data lineage, governance, and reproducibility concepts

Responsibilities

• 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

Skills

SQL

Python

Data modeling

ML dataset creation

Education

Doctorate degree

Master’s degree

Bachelor’s degree

Tools

Modern data engineering platforms

ETL tools

Job description

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

Job Summary

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.

Responsibilities

• 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

Qualifications

Basic Qualifications:

• Doctorate degree PhD with 5+ years of relevant exp
• Or Masters degree and 8+ years of directly related experience
• Or Bachelors 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