Senior Data Scientist - R&D DDSAI - Small Molecule Discovery
Jj · Spring House, Pennsylvania, United States
At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com
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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 SummaryAs 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 ResponsibilitiesScientific 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.
- Foster collaboration across scientific, technical, and data-focused teams.
What Success Looks LikeHow the role builds an essential part of our R&D engine
Trusted discovery data assets-
Molecular, reaction, assay, and process data are consistently available, documented, and reusable.
AI-ready scientific data-
High-quality, model-ready datasets accelerate analytics, machine learning, and AI initiatives.
Improved scientific decision-making-
Scientists can more easily access and interpret critical discovery information.
Connected discovery workflows-
Data from synthesis, purification, assay, and automation workflows are integrated and accessible.
Sustainable technical foundations-
Scalable solutions support long-term growth of discovery data and AI capabilities.
QualificationsRequired- Master's degree or Ph.D. in Chemistry, Cheminformatics, Data Science, Computer Science, Pharmaceutical Sciences, Chemical Engineering, or a related scientific discipline.
- 5+ years of industry experience in pharmaceutical, biotechnology, or life sciences research environments.
- Strong understanding of small-molecule discovery workflows, including molecular structures, reaction data, synthesis workflows, and structure-activity relationships.
- Advanced proficiency in Python and SQL.
- Experience developing solutions on modern cloud data platforms, including Databricks and Snowflake.
- Significant experience working with molecular and reaction data using scientific software libraries and toolkits such as RDKit.
- Experience designing analytical workflows and data models for complex scientific datasets.
- Understanding of data engineering concepts including data modeling, metadata management, lineage, reproducibility, and data quality.
- Ability to collaborate effectively with scientists, engineers, and technology partners in a matrixed R&D environment.
- Strong communication skills and the ability to translate between scientific and technical audiences.
Preferred- Experience supporting medicinal chemistry, synthesis, compound registration, or screening workflows.
- Familiarity with automated synthesis, laboratory automation, and high-throughput experimentation environments.
- Experience working with reaction informatics, reaction registration, or scientific workflow platforms.
- Experience with OpenEye scientific software.
- Experience developing machine learning solutions for molecular, reaction, or experimental datasets.
- Familiarity with FAIR data principles and scientific data governance practices.
- Experience working with graph-based, semantic, or ontology-driven approaches to scientific data integration.
- Knowledge of modern software engineering practices including testing, version control, CI/CD, and cloud-native development.
Why This Role Is UniqueThis is an opportunity to shape how small-molecule discovery data enables the next generation of scientific research and AI-driven drug discovery. You will work at the in