Data Innovation Specialist (RWD)

Roche•South San Francisco, CA

9d•$97,900 - $181,700•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. 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 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 Head, 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

Hold a Bachelor’s or Master’s degree in Data Science, Informatics, Life Sciences, or a related technical field
Minimum of 2-4 years of experience working on data-related or innovation-support projects, or an advanced degree with 0-2 years of equivalent work experience
Basic familiarity with scripting languages (e.g., Python or R) and version control tools (e.g., Git)
Able to work collaboratively and document technical findings clearly
Demonstrate capacity for independent thinking and ability to make decisions based upon sound principles
Bring excellent strategic agility including problem-solving and critical thinking skills, and agility that extends beyond the technical domain
Demonstrate respect for cultural differences when interacting with colleagues in the global workplace
Excellent verbal and written communication skills, specifically in the areas of presentation and writing, with the ability to explain complex technical concepts in clear language

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.
Support robust data engineering activities to design, optimize, and streamline data workflows, building scalable data pipelines that integrate unstructured, multi-modal, and unconventional data sources.
Support innovation activities and 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.
Enable 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 partner, 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.