Roche
Data Scientist
Overview
Design, build, and deploy production-ready AI/ML solutions and agentic workflows, taking models from initial prototype to robust, monitored enterprise capabilities.
About Roche
Roche is a global pioneer in pharmaceuticals and diagnostics focused on advancing science to improve people's lives. Roche Digital Technology (RDT) is a global team at the heart of Roche, committed to shaping the digital future of healthcare through artificial intelligence, data, and scalable tech innovations.
Requirements & Eligibility
- Strong hands-on proficiency in Python, LLMs, RAG pipelines, vector databases/embeddings, and core NLP/statistical modeling with clean, reproducible code
- Proven ability to build evaluation harnesses for GenAI, perform model fine-tuning, and implement LLMOps/MLOps pipelines with full observability
- Demonstrated skill in bridging technical implementation with non-technical stakeholders, explaining complex AI trade-offs, and managing infrastructure/cost optimization
- Hands-on experience deploying scalable enterprise AI solutions on AWS or Azure platforms
- Experience with AI governance, model risk management, fairness/bias mitigation, or working within healthcare, pharma (GxP), or financial environments
Key Responsibilities
- Design, build, and deploy production-ready AI/ML solutions and agentic workflows, taking models from initial prototype to robust, monitored enterprise capabilities.
- Turn complex, ambiguous business problems into practical modeling approaches, selecting the optimal tool rather than relying on standard LLMs by default.
- Establish rigorous evaluation frameworks, golden datasets, and testing harnesses to offline- and online-evaluate GenAI/LLM outputs for safety, reliability, hallucination drift, and performance.
- Build MLOps/LLMOps pipelines for continuous integration, deployment, and real-time observability, while managing infrastructure costs, token usage, and vendor API integrations.
- Embed Roche's AI governance, security, and compliance guardrails directly into development pipelines, ensuring fairness, privacy, and explainability from day one.
- Partner closely with product owners, cross-functional engineers, and business leaders to translate technical trade-offs and surface data-driven insights on AI adoption, impact, and value.
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