Principal Scientific Software Engineer

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.

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) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximizing these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness this transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.

The Analytics and Workflows group within the Center of Excellence (CoE) is dedicated to turning complex data into actionable insights that advance drug discovery and development. We leverage cutting-edge high-throughput technologies and foundational multimodal machine learning models to analyze large-scale biological data, enabling deeper understanding of disease mechanisms and the identification of novel therapeutic opportunities. Despite advances in these fields, however, transforming raw data and computational models into meaningful biological insights remains a key challenge. In this role, you’ll work at the intersection of data science, biology, and engineering to build innovative analytical tools that bridge the gap between data generation and biological interpretation—helping unlock new avenues for scientific discovery and impact.

The Opportunity

Within this group, you’ll collaborate with a cross-functional team to design, develop, and deploy robust tools that empower scientists to explore, visualize, and interpret complex biological datasets and models—ranging from spatial transcriptomics and high-throughput chemical screens to real-world clinical data. While you won’t be expected to be an expert across the full technology stack, we value fluency across boundaries. Depending on your background, your work may focus more on front-end or backend engineering, and could include:

• 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

• Building scalable, high-performance data pipelines and backend systems, including APIs and services, to efficiently process and deliver large-scale, multimodal biological data.

• 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

Who you are

• 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.

• 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

• 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)

• Preferred expertise building intuitive front-end applications using modern JavaScript or TypeScript frameworks (e.g., Svelte, Vue, React), with seamless int