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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, Madrid, Spain, Spring House, Pennsylvania, United States of America, Toledo, Spain

Job Description:

Johnson & Johnson Innovative Medicine is recruiting for a Senior Scientist, Discovery Data Science to help advance Artificial Intelligence/Machine Learning (AI/ML) for Drug Discovery. The preferred location for this position is our R&D site in Beerse, Belgium, but we will also consider location in our R&D site in Springhouse, PA, USAR&D sites in Spring House, PA. The position offers the possibility for hybrid work with a 3 day on campus expectation.

 

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow. Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Learn more at https://www.jnj.com/innovative-medicine

 

Within IM R&D, the Therapeutics Discovery organization is continuing to build the key scientific expertise to bring ground-breaking new medicines to patients; we invest in medicinal chemistry, screening, and pharmacology, and in the computational and data science fields that inspire and inform them.

 

Many aspects of the drug discovery process are being partially virtualized: modelling methodologies, prescreen of existing or virtual molecules for relevant properties, and focusing experimental bandwidth to make and test those molecules that hold the most promise.

 

The Senior Scientist will invent, internalize, and apply creative yet scientifically sound data science solutions to virtually assess molecules in the broadest possible sense. The position’s scope includes methodological innovation as well as crafting data generation, acquisition, and alliance strategies.

 

The successful candidate will be actively involved in method development and project support in collaboration with data scientist, computational small molecule scientists, assay development scientists and medicinal chemists, project biologists, toxicology and pharmacokinetics and IT colleagues.

 

KEY RESPONSIBILITIES INCLUDE:

• Drive and contribute to the development and deployment of next‑generation AI/ML solutions that shape the future of drug discovery. You will develop predictive models that explore chemical space within and beyond Lipinski’s Rule of 5, enabling smarter design of synthetic molecules across diverse therapeutic areas.

• Transform ideas into real‑world impact by collaborating with cutting‑edge IT and MLOps teams to bring your models into production environments—where they directly influence scientific decisions and accelerate discovery pipelines.

• Translate complex computational insights into clear, compelling stories, enabling colleagues from different scientific backgrounds to understand and act on your findings.

• Work side‑by‑side with experimental teams and digital technologists, integrating machine learning and AI into laboratory workflows to unlock new biological insights, optimize experiments, and reveal actionable design strategies.

• Thrive in a collaborative, inclusive team culture built on trust, creativity, openness, and scientific curiosity—where your ideas are valued and your voice matters.

• Contribute to the global scientific community by publishing your work in peer‑reviewed journals and presenting at leading conferences, showcasing innovative approaches that push the boundaries of computational drug discovery.

 

 

QUALIFICATIONS

• PhD in Data Science, Computational Science, or a related drug‑discovery field, with strong, hands‑on expertise in data science, machine learning, and modern AI approaches.

• Proven ability to design, evaluate, and customize deep learning architectures to tackle complex scientific challenges and deliver innovative, high‑impact solutions.

• Proficiency in Python, with experience using major deep‑learning frameworks such as PyTorch, TensorFlow, or Keras, and strong working knowledge of scientific libraries (NumPy, SciPy, Pandas). Experience or affinity with cloud computing and MLOps practices is highly valued.

• Experience in data analytics, predictive modeling, and AI-assisted‑assisted molecular design and optimization for small molecules (desired).

• Experience applying AI/ML to off-t‑target risk prediction or liability modeling in drug discovery (desired).

• Familiarity with biology and/or chemistry, and enthusiasm for learning across scientific domains (desired).

• Demonstrated ability to thrive in cross‑functional, matrixed project teams, contributing effectively to multidisciplinary research.

• A track record of planning, executing, and delivering complex scientific projects, with strong analytical thinking and scientific rigor.

• Genuine passion for applying computational science to real‑world industrial problems, especially in the context of improving human health through drug discovery.

• Strong communication, organizational, and interpersonal skills, including the ability to collaborate, present results clearly, and engage with diverse scientific partners.

• A commitment to being part of a team that values diversity, fosters inclusion, and encourages creativity, innovation, and continuous learning.

• Any earlier experience in AI-driven molecular design and optimization, related predictive modelling, data analytics or liability modelling, or exposure to applied biology or chemistry would strengthen an application. 

  

Optionally, the position title commensurate with alternative experience level.

 

 

Required Skills:

 

 

Preferred Skills:

Advanced Analytics, Business Intelligence (BI), Coaching, Collaborating, Critical Thinking, Data Analysis, Database Management, Data Privacy Standards, Data Reporting, Data Savvy, Data Science, Data Visualization, Econometric Models, Process Improvements, Technical Credibility, Technologically Savvy, Workflow Analysis