Senior Data Scientist - R&D DDSAI - Small Molecule Discovery

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Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Science

Job Category:

Scientific/Technology

All Job Posting Locations:

Beerse, Antwerp, Belgium, Cornellà de Llobregat, Barcelona, Spain, Madrid, Spain, Spring House, Pennsylvania, United States of America

Job Description:

Johnson & Johnson Innovative Medicine is seeking a Senior Data Scientist dedicated to our Small Molecule Discovery organization. This role sits within our Data Science team and partners closely with medicinal chemists, automation scientists, data engineers, computational scientists, and technology teams to ensure that small-molecule discovery data are transformed into trusted, reusable, AI-ready assets.

The role operates at the intersection of scientific discovery, data science, and scientific data engineering. You will help shape how molecular, synthesis and purification, and assay data are represented, connected, and leveraged to support scientific decision-making, advanced analytics, machine learning, and emerging AI capabilities throughout the discovery lifecycle.

Why this role matters: Small Molecule Discovery generates large volumes of complex and interconnected data across design, synthesis, purification, characterization, and testing workflows. Realizing the full value of these data requires more than traditional analytics. It requires deep understanding of how scientific workflows generate data, how those data should be modeled and governed, and how they can be transformed into reusable assets for decision support, machine learning, and AI-enabled discovery.

This role serves as a critical bridge between scientific workflows and modern data platforms, ensuring that discovery data can be effectively leveraged to accelerate insight generation, improve data quality, and enable next-generation AI applications developed by our partners in our In Silico Discovery.

Position Summary

As a Senior Data Scientist, Small Molecule Discovery, you will design and develop data assets, analytical solutions, and scientific data workflows to support discovery research across the Design-Make-Test-Analyze cycle.

You will work closely with discovery scientists, automation teams, and technology partners to transform complex scientific data into scalable, well-governed, and AI-ready resources. You will bring deep understanding of small-molecule discovery data and modern data science practices to help establish robust foundations for analytics, machine learning, and digital laboratory initiatives.

Key Responsibilities

Scientific Data Products & Analytics

• Design, develop, and maintain high-quality data assets that support small-molecule discovery workflows.
• Transform molecular, synthesis and purification, and assay data into reusable datasets that support scientific decision-making and machine learning applications.
• Partner with scientists and technology teams to define data requirements, business rules, and analytical use cases.
• Develop scalable analytical workflows using modern cloud data platforms and scientific computing tools.
• Apply data science methodologies to extract insights from complex discovery datasets.

Discovery Data & AI Readiness

• Ensure scientific data are structured, documented, and represented in ways that support advanced analytics and AI applications.
• Contribute to the development of data standards, metadata practices, and data quality frameworks for discovery data.
• Partner with data engineering teams to ensure discovery data products are scalable, traceable, and reproducible.
• Support the creation of model-ready datasets for machine learning and scientific AI initiatives.
• Evaluate emerging opportunities to apply AI and advanced analytics within discovery workflows.

Scientific Partnership & Technical Leadership

• Serve as a trusted technical partner to medicinal chemists, automation scientists, and in silico discovery teams.
• Translate scientific workflows and requirements into computational and data solutions.
• Contribute to best practices in scientific computing, data management, and reproducible research.
• Mentor colleagues on modern data science approaches and scientific data practices.

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