Roche's Research and Early Development organizations at Genentech (gRED) and Pharma (pRED) need to maximize the opportunities presented by advances in AI, data, and computational sciences to accelerate R&D. Seamless data sharing and access to models across gRED and pRED are essential. The new Computational Sciences Center of Excellence (CoE) aims to harness the power of data and AI to assist scientists in delivering more innovative medicines. The Analytics and Workflows group within the CoE faces the challenge of transforming raw data and computational models into meaningful biological insights, requiring innovative analytical tools to bridge the gap between data generation and biological interpretation.
Requirements
- Experienced in building static and interactive visualizations using modern libraries and tools (e.g., D3.js, Vega-Lite, WebGPU, Plotly; Shiny, ggplot2; Streamlit, Dash, Altair, Seaborn, Bokeh)
- Expertise building intuitive front-end applications using modern JavaScript or TypeScript frameworks (e.g., Svelte, Vue, React), with seamless integration of interactive web-based visualizations (e.g., D3.js, WebGL, WebGPU)
- Proficient in using Python and/or R to transform and analyze complex biological datasets, with a solid understanding of software engineering principles such as modular design, testing, and version control in collaborative, production-oriented environments
- Optional experience in backend development with Python, including building scalable APIs using frameworks such as FastAPI and GraphQL
- Preferred familiarity with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes)
- Comfortable collaborating with AI/ML scientists to integrate outputs from machine learning models—such as embeddings, classifications, or generative outputs—into user-facing tools that enable model interpretability and interaction
- Bonus: Experience designing tools or workflows that leverage AI—such as LLMs or agentic systems—to assist with biological data exploration and interpretation
Responsibilities
- Engaging directly with scientists—at the bench or the keyboard—to understand and clarify emerging (and sometimes ambiguous) analytical needs
- Identifying, evaluating, and applying emerging technologies to analyze and visualize biological and chemical data in support of drug discovery and development
- Designing and implementing modular, extensible platforms that allow biologists and data scientists to access, share, and interpret computational results, abstracting technical complexity while enabling scientific insight
- Creating intuitive, interactive visualizations that integrate diverse biological data types (e.g., spatial imaging, transcriptomic, clinical), helping scientists explore hypotheses and uncover insights
- Collaborating across distributed scientific, engineering, and design teams to support end-to-end development—from early exploration to production-ready applications
- Comfortable collaborating with AI/ML scientists to integrate outputs from machine learning models—such as embeddings, classifications, or generative outputs—into user-facing tools that enable model interpretability and interaction
Other
- Ph.D. in Data Science, Mathematics, Statistics, Computer Science, Life Sciences, Chemistry, Public Health, or a related field, with 2+ years of relevant experience; alternatively, a Master’s degree with 5+ years of relevant experience
- Strong analytical intuition for extracting meaning from complex datasets, coupled with the ability to communicate insights effectively across disciplines and backgrounds.
- Curious, eager to grow new skills, and excited to explore emerging technologies while collaborating across diverse, multidisciplinary teams
- This position requires being onsite 3 days a week.
- Please provide a Cover letter and a Link to your personal website, URL and GitHub in your application
