Data Scientist -AI Drug Discovery Full-time NEW

Data Scientist In Ai Drug Discovery

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it's work worth doing. If you're driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

At Lilly, technology is not a support function. It is how a global medicine company operates, innovates, and delivers. Lilly in Bengaluru builds the capabilities that make this possible, cloud platforms, AI systems, and automation at enterprise scale, all in service of a purpose that makes this technology work genuinely distinctive, from advancing drug discovery to enabling connected clinical trials to keeping a global medicine company running at the standard patients deserve.

As a Data Scientist in AI Drug Discovery, you will use data science and machine learning to help answer consequential questions in drug discovery: which targets to pursue, which perturbations to test, and how cellular systems may respond.

A core focus of this position is single-cell biology and virtual cell modelling. You will develop and evaluate in silico models of cellular state and response, connect predictions to biological evidence, and translate results into clear recommendations for experimental scientists. You will join a multidisciplinary environment spanning biology, chemistry, computational science and engineering. The role is ideal for someone who enjoys turning an ambiguous scientific question into a rigorous computational problem, challenges their own conclusions, and delivers reusable work that scientists can trust and apply.

Key skills required:

• Strong Python skills and practical experience with a machine learning and deep learning networks.
• Solid grounding in machine learning, model validation, and experimental design.
• Working knowledge of transformers or graph neural networks and of sound train/validation/test design.
• Experience working with large, noisy or heterogeneous scientific and biomedical datasets.
• Experience with single-cell omics, genetics, chemistry or imaging data.
• Experience running training and analysis on HPC, GPU or cloud infrastructure.
• Ability to communicate technical findings, uncertainty, and limitations to collaborators from different disciplines.

Key responsibilities include:

• Develop and validate models. Build, train, benchmark and validate machine learning models for scientific data, including single-cell omics, genetics, chemistry and imaging. Select methods based on the scientific question, from classical machine learning to deep learning and foundation models.
• Advance virtual cell modelling. Develop and evaluate models that predict cellular state and response to genetic or chemical perturbations. Use them for in silico screening and prioritise high-value experiments.
• Support target identification. Combine model outputs, perturbation evidence and prior biological knowledge to nominate and rank candidate targets. Communicate strength of evidence, uncertainty, limitations and proposed experimental validation.
• Create analysis-ready data. Build reproducible workflows to ingest, harmonise, quality-control and document public and internal datasets across formats and modalities.
• Integrate scientific knowledge. Incorporate regulatory and interaction networks, ontologies, pathways and other biological or chemical priors into models, and quantify the contribution of each information source.
• Scale and operationalise work. Run training and analysis on HPC, GPU and cloud infrastructure using versioned code, containers, automated testing and reusable software components.
• Collaborate and influence. Partner with scientists and engineers to frame questions, define decision criteria, and interpret findings. Present clearly to technical, scientific, and leadership audiences.

Basic qualifications:

• Master's degree in Machine Learning, Computer Science, Computational Chemistry, Computational Biology, Physics, Statistics, or a closely related quantitative field.
• 3+ years of experience applying machine learning in industry or academia, including delivering end-to-end projects and contributing to research or technical direction within a team.
• Proficiency in Python and modern ML frameworks such as PyTorch, with hands-on experience developing and evaluating models.

Desirable skills:

• Experience with single-cell and perturbation data (e.g., Perturb-seq, CRISPR screens) and virtual cell or foundation models.
• Familiarity with statistical genetics methods (GWAS, fine-mapping, colocalization) and multi-omics integration.
• Experience with knowledge graphs, ontologies, pathway databases, or other structured biological priors.

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.