Design and build robust computational and data ecosystems supporting drug discovery. Lead the development of scalable applications and tools, integrating diverse scientific data. Ideal for experienced software engineers with deep expertise in life sciences and a passion for advancing data-driven science.
Advances in AI, data and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organizations at Genentech (gRED) and Pharma (pRED) leverage these technologies to accelerate R&D, using data and computational models to drive impact. The Computational Sciences Center of Excellence (CS CoE) aims to harness the power of data and AI to assist scientists in delivering innovative medicines for patients worldwide. The Data and Digital Catalyst (DDC) organization leads the modernization of computational and data ecosystems by integrating digital technologies across Research and Early Development to empower stakeholders, advance data-driven science and accelerate decision-making.
Tasks
- Design and build interactive tools for small and large molecule drug discovery, integrating data, molecular design workflows, computational algorithms, and AI models.
- Build and enhance a modern, scalable ecosystem of applications and tools to capture, move, and prepare scientific data for analysis.
- Design and deliver complex, user-friendly data applications and pipelines that integrate diverse data sets across key scientific domains.
- Leverage standard patterns like microservices and event-driven architecture to improve data workflows and the application ecosystem.
- Champion technical excellence by ensuring solutions are innovative, best-in-class, and seamlessly integrated.
- Integrate diverse data sets to power AI, machine learning, and Natural Language Search capabilities.
- Collaborate directly with computational and research scientists to understand their challenges and translate them into effective technology solutions.
- Provide technical leadership on ongoing initiatives and identify new opportunities to build common, reusable solutions.
- Mentor junior developers, fostering their professional growth and upholding high engineering standards.
Requirements
- Passion for building scalable platforms for drug discovery or a related domain.
- Experience engaging with scientific partners to solve complex challenges.
- Dedication to engineering best practices.
- Ph.D. or equivalent experience of 8-10+ years working in the life science domain building full-stack software solutions, preferably for drug discovery.
- Strong software engineering expertise in all layers of the stack, including backend (e.g., Python, Java) and frontend (e.g., React, Vue).
- Deep experience with modern data engineering, including databases (SQL/NoSQL), data engineering (ETL/ELT), and APIs (REST, GraphQL).
- Proven experience developing and deploying applications on cloud-native architectures, preferably AWS.
- Experience in setting up SDLC best practices for the team, including versioning, testing, observability, CI/CD, etc.
- Exceptional abilities to architect scalable solutions in complex environments with multiple existing system integrations.
- Demonstrated proficiency in applying AI-assisted and agentic coding practices, both as an individual contributor and collaboratively within a team environment.
- Outstanding communication and collaboration skills, with the ability to articulate technical concepts to diverse audiences.
- Preferably experience working in pharmaceutical or a related domain and in setting best practices for agentic coding producing maintainable code.
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Das Team
The Solutions team within the Data and Digital Catalyst Organization develops modernized and interconnected computational and data ecosystems. The team specializes in building data pipelines and applications for data acquisition, collection, storage, transformation, linkage, and sharing, supporting computational and bench scientists and enabling ML/AI tool creation and adoption.