Principal Data Innovation Specialist (RWD)

Genentech•Daly City, CA

6d•$140,900 - $261,700•Onsite

About The Position

This role is based in the Real-World & Clinical Data Strategy (RWCDS) team which drives data excellence and analytics to power enhanced decision-making and evidence-generation within Roche Product Development (PD). We develop and implement data strategies that unlock the value of clinical, operational, and real-world data to accelerate evidence generation, decision-making, and innovation across PD and beyond. By embedding FAIR principles, advancing data governance, and enabling data productization, RWCDS ensures that data is not only findable and usable, but also a driver of scientific rigor, regulatory readiness, and AI-powered transformation. Through deep partnerships with internal functions and external collaborators, we shape a future-ready data ecosystem that supports personalized healthcare and efficient drug development. The Principal Data Innovation Specialist (RWD) helps identify, prototype, and scale novel methods and technologies that transform how data is used across product development. Reporting to the Data Innovation Lead, you will explore emerging capabilities such as AI, semantic integration, and knowledge graphs to unlock new use cases for clinical, real-world, and operational data. You will collaborate with scientists, engineers, product teams, and external partners to design proof-of-concepts, validate innovative solutions, and assess long-term feasibility. This role requires curiosity, strong technical grounding, and a mindset oriented toward experimentation and continuous learning.

Requirements

Master’s or PhD in Computer Science, Engineering, Data Science, or a related field
6+ years of experience across data engineering, data science, or healthcare innovation settings
Proven success implementing innovation pilots
Deep practical experience in AI-driven analytics, multi-agent architectures, and the application of engineering frameworks in complex research or industry environments
Highly proficient with agile development, product-thinking, and innovation lifecycle frameworks (such as Lean Startup or Design Thinking)
Experience evaluating new data platforms, APIs, or data partnerships
Exceptional critical thinking, problem-solving skills, and independent decision-making capabilities that extend across both technical engineering domains and broader strategic areas
Excellent presentation and communication skills to explain complex technical concepts simply
Thrive in a culturally diverse global workplace
Actively contribute to internal or external biomedical innovation communities

Nice To Haves

Exposure to real-world data or research informatics in a biomedical setting
Interest in emerging technologies such as machine learning, knowledge graphs, or clinical AI
Ability to summarize results for both technical and non-technical audiences

Responsibilities

Prototype, validate, and scale advanced AI capabilities, focusing heavily on multi-agent development and orchestration architectures to unlock complex use cases across product development.
Lead robust data engineering activities to design, optimize, and streamline data workflows, building scalable data pipelines that integrate unstructured, multi-modal, and unconventional data sources.
Drive innovation activities and lead complex analytical projects focused on exploring novel data types, automated agent-driven workflows, and knowledge graph integrations in a multi-modal environment.
Partner with scientists, engineers, and product teams to pilot data innovations, integrating advanced engineering pipelines and multi-agent frameworks into broader clinical development and data science workflows.
Drive the development of low-/no-code analytics platforms to democratize multi-modal data access and establish frameworks for the scalable evaluation, productization, and reuse of experimental data approaches.
Serve as an innovation methodology expert, guiding peers through new data modalities while mentoring junior team members on cutting-edge techniques, data tools, and agent architectures.
Proactively identify innovation areas via research while ensuring all engineering and AI frameworks maintain absolute compliance with data privacy and security regulations like HIPAA and GDPR.