Associate Director & Senior Principal Data Science, AI for Biologics
At a glance
Director-level Data Science role at Novartis. Hyderabad · full-time.
Pay not stated Data Science salary
dataroles summary, based on the employer's posting.
What you'll do
- Turn drug-discovery questions into AI studies with clear hypotheses, evaluation plans and measures of success
- Build and assess AI approaches for protein structure, biological foundation models and generative molecular design
- Run proof-of-concept work that compares computational results with experimental validation
- Judge emerging models, datasets and technologies for their scientific limits and value in drug discovery
- Advise senior stakeholders on technology choices, and mentor scientists translating AI advances into discovery impact
What you bring
- A doctorate or equivalent experience in machine learning, computational biology or another listed discipline
- At least five years applying machine learning and AI to complex scientific or biomedical problems
- Hands-on use of protein language models, generative models or other AI with biomolecular sequence and structure data
- Deep biologics or antibody discovery expertise, including how computational predictions inform experiments and development
- A record of rigorous model evaluation and scientific leadership that connects AI work to drug-discovery impact
Who this fits
This senior scientific leadership role suits someone who can connect AI methods, protein science and experimental decisions. It includes mentoring and influencing cross-functional and senior stakeholders. The position is in Hyderabad, India, and relocation support is available.
From the employer
Job Description Summary
Job Title: Associate Director & Senior Principal Data Science, AI for BiologicsLocation: 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 diversityandincl.india@novartis.com 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