Associate Director & Senior Principal Data Science, AI for Biologics

Novartis AG; Hyderabad; Hybrid; INR 3,500,000 - 6,000,000; Full time

Benefits offered by this job

Relocation Support

Job summary

Novartis AG in Hyderabad, India seeks an Associate Director & Senior Principal Data Science to lead AI-driven biologics initiatives, including antibody discovery, protein engineering, and design. You will translate scientific questions into high-value AI opportunities and guide collaborations with academia, industry, and internal teams.

The role emphasizes translating AI advances into drug-discovery impact, mentoring scientists, and shaping strategic AI directions for biomolecular research.

Qualifications

• PhD or equivalent in ML, computational biology, bioinformatics, structural biology, protein engineering, or related field.
• 5+ years translating complex scientific challenges into ML/AI solutions.
• Experience applying protein language models, generative models or other AI methods to biomolecular data.
• Deep expertise in biologics or antibody discovery, linking computational predictions to experiments.

Responsibilities

• Lead AI-enabled biologics and antibody discovery initiatives.
• Translate drug-discovery challenges into well-defined AI opportunities with clear hypotheses and evaluation criteria.
• Develop and evaluate AI methods for protein structure prediction and generative design.
• Assess emerging AI models and technologies for drug-discovery value.
• Mentor scientists and drive cross-functional proof-of-concept studies.

Skills

AI

Biostatistics

Data Science

Deep Learning

Generative AI

NLP

Machine Learning

SQL

Python

R Programming

Pandas Python Library

Education

Doctoral degree in ML, computational biology, bioinformatics, structural biology, or related

Job description

Job Description Summary

Job Title: Associate Director & Senior Principal Data Science, AI for Biologics

#LI-Hybrid

Location: Hyderabad, India

Relocation Support: Yes

What if your expertise in AI and protein science could help shape the next generation of biologics? In this highly influential scientific role, you will help advance how AI is applied to biologics, antibody discovery and engineering, and protein structure and design. Working at the intersection of cutting-edge AI and drug discovery, you will partner with scientists, data scientists, engineers and external research leaders to evaluate emerging technologies, turn complex scientific questions into high-value AI opportunities, and translate innovation into measurable impact for patients.

Job Description

Key Responsibilities

• Lead scientific innovation in AI-enabled biologics, antibody discovery, protein engineering, and protein design.
• Translate complex drug-discovery challenges into well-defined AI opportunities with clear hypotheses, evaluation methods, and success criteria.
• Develop and evaluate rigorous AI methods for protein structure prediction, biological foundation models, and generative molecular design.
• Assess emerging AI models, datasets, and technologies for scientific maturity, differentiation, limitations, and drug-discovery value.
• Lead cross-functional proof-of-concept studies connecting computational performance with experimental validation and meaningful scientific outcomes.
• Partner with engineering, product, data, and platform teams to transform successful innovations into reusable AI capabilities.
• Build strategic collaborations with academia, biotechnology companies, technology organizations, and external research partners.
• Provide evidence-based recommendations on which emerging technologies to explore, partner on, scale, internalize, or deprioritize.
• Influence scientific strategy by communicating evidence, uncertainty, trade-offs, risks, and potential value to senior stakeholders.
• Mentor scientists and data scientists, strengthening scientific judgment and translating AI advances into drug-discovery impact.

Essential Requirements

• Doctoral degree or equivalent experience in machine learning, computational biology, bioinformatics, structural biology, protein engineering, or a related discipline.
• At least five years' hands-on experience translating complex scientific or biomedical challenges into machine learning and artificial intelligence solutions.
• Practical experience applying protein language models, generative models, or other artificial intelligence methods to biomolecular sequence and structure data.
• Deep expertise in biologics or antibody discovery, connecting computational predictions with experimental decisions and downstream development considerations.
• Proven experience designing rigorous model evaluation and validation studies, including benchmarking, prospective testing, or experimental validation.
• Demonstrated scientific leadership, influencing cross-functional stakeholders and translating artificial intelligence innovation into measurable drug-discovery or portfolio impact.

Desirable Requirements

• Knowledge of molecular dynamics, statistical potentials, or structural bioinformatics applied to protein and biologics research.
• Strong scientific standing demonstrated through publications, patents, external collaborations, invited presentations, open-source contributions, or professional leadership.

Commitment to Diversity and Inclusion:

Novartis is committed to building an outstanding, inclusive work environment and diverse teams' representative of the patients and communities we serve.

Accessibility and accommodation

Novartis is committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the recruitment process, or in order to perform the essential functions of a position, please send an e-mail to [e-mail] and let us know the nature of your request and your contact information. Please include the job requisition number in your message

Skills Desired

• Artificial Intelligence (AI)
• Biostatistics
• Business Value Creation
• Change Management
• Curious Mindset
• Data Governance
• Data Literacy
• Data Quality
• Data Science
• Data Visualization
• Deep Learning
• Generative AI
• Graph Algorithms
• Learning Agility
• Machine Learning (ML)
• Machine Learning Algorithms
• Neuro-Linguistic Programming (NLP)
• Organizational Awareness
• Pandas Python Library
• Python (Programming Language)
• R Programming
• Stakeholder Engagement
• Statistical Analysis
• Structured Query Language (SQL)
• Time Series Analysis