Computational Infrastructure Scientist – Biological Data Systems

Computational Infrastructure Scientist – Biological Data Systems

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At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first.

We’re looking for people who are determined to make life better for people around the world. We are seeking a Computational Infrastructure Scientist to help build and extend the data systems that power large-scale biological and genetic research — the foundations of modern biology and precision medicine. This is a scientific engineering position, ideal for a PhD-level scientist who bridges biology, computation, and informatics.

The successful candidate will have a deep understanding of the complexity and diversity of biological and genetic data, and a passion for designing robust, reusable infrastructure that makes this data usable, reproducible, and scalable across research and discovery pipelines.

• Developing and extending data models and ontologies that harmonize heterogeneous sources of biological, genetic, and clinical information.
• Designing scalable data ingestion, standardization, and transformation pipelines for genomic and functional datasets.
• Collaborating with domain experts in genomic medicine, computational biology, bioinformatics, genetics, and informatics to ensure that infrastructure design supports high-impact scientific use cases.
• Support and develop scientific workflows that impact the drug discovery portfolio.
• Building the frameworks that enable semantic interoperability, metadata-rich data exchange, and traceable, reproducible data workflows.
• Contributing to the evolution of open data standards and knowledge representation across the life sciences.
• The ideal candidate combines scientific curiosity with engineering discipline — someone who finds beauty in well-designed systems and efficiency in well‑structured data.

• Strategize and implement scientific data processing workflows that transform complex biological datasets into actionable insights.
• Design and develop innovative algorithms and ETL systems to address emerging challenges in biological and drug discovery data integration.
• Collaborate cross-functionally with domain scientists and engineers to translate biological questions into computational frameworks.
• Contribute to the long‑term architecture and evolution of the data platform, ensuring scalability, transparency, and reproducibility.
• Develop cloud‑based workflows and APIs that enable efficient access and analysis across diverse biological datasets.
• Document and share design decisions to promote reuse and institutional knowledge.

• PhD in Computational Biology, Chemistry, Bioinformatics, or a related scientific field.

• Strong programming experience in Python and strong familiarity with R.
• Experience working in Linux environments.
• Knowledge of biological databases, ontologies, and metadata systems.
• Knowledge in Postgre
  
  SQL databases.
• Proficiency in Linux environments and Git (required).
• Exposure to cloud platforms (e.g., AWS S3, EC2, or equivalent).
• Experience working with workflow execution environments including Next Flow.
• Experience developing data-driven decision support applications including data and visual analytical tools.
• Exposure to Docker or containerized environments.
• Strong communication skills and the ability to work independently on open-ended technical problems.
• Understanding of web design and API is a plus.

• A unique opportunity to shape the data infrastructure that underpins next‑generation biological research.
• Cross‑functional exposure with state‑of‑the‑art discovery.
• A role combining scientific insight with engineering autonomy.
• Hands‑on…