Associate Director & Senior Principal Data Science, AI for Biologics

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.