Computational Scientist – Digital R&D BioAIM Data Science

Computational Scientist – Digital R&D BioAIM Data Science page is loaded## Computational Scientist – Digital R&D BioAIM Data Science locations:
Barcelona time type:
Vollzeitposted on:
Heute ausgeschriebentime left to apply:
Enddatum: 6. März 2026 (Noch 15 Tage Zeit für Bewerbung) job requisition :
R2844202
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* Location:

Barcelona*##
** About Sanofi:
** At Sanofi, we’re committed to providing the next-gen healthcare that patients and customers need. It’s about harnessing data insights and leveraging AI responsibly to search deeper and solve sooner than ever before.

Sanofi has recently embarked on a vast and ambitious digital transformation program. A cornerstone of this roadmap is the acceleration of its data transformation and of the adoption of artificial intelligence (AI) and machine learning (ML) solutions to accelerate R&D, manufacturing and commercial performance, and bring better drugs and vaccines to patients faster, to improve health and save lives.
In alignment to our digital transformation, we have launched strategic initiatives under the umbrella of  the Biologics x AI Transformation. This is positioned to be a unique data-driven team, with expertise in AI platforms, data engineering, ML operations, data science, computational biology, strategy, and beyond. We are working as one to identify, design, and scale state-of-the-art AI capabilities targeted to truly transform how we do research.##

** Who You Are:
** You are an expert Computational Scientists with strong engineering skillsets who will work with other scientists and engineers to develop and deploy cutting-edge computation, Machine Learning/Deep Learning, and data engineering approaches to revolutionize our computational drug discovery tools by contributing to accelerating and improving the process of design and engineering of novel drugs.##
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* Main Responsibilities:

*** Develop, improve and deploy on our internal platform artificial intelligence and machine learning (AI/ML) approaches (e.g. classification, clustering, deep learning, foundation models, diffusion models, flow matching etc) on pharma research data sets (e.g. activity, function, ADME properties, physico-chemical properties, etc.)
* Building models from internal and external data sources, algorithms, simulations, and performance evaluation by writing clean code and using state-of-the art machine learning technologies
* Collaborate on developing intuitive user interfaces for AI tools, ensuring that complex ML/AI capabilities are accessible to scientists through interactive visualizations, and seamless user experiences that accelerate adoption across research teams
* Develop and deploy Agentic AI systems. This includes building agent architectures with tool integration, and orchestrating multi-agent workflows to automate complex scientific processes
* Close interactions with other Computational scientists, data engineers, software engineers, UX designers, as well as research scientists in core scientific platforms focusing on protein therapeutics, in an international context
* Update and report relevant results to interdisciplinary project teams and stakeholders
* Maintain a keen awareness of recent developments in data science and bioinformatics and state-of-the-art of AI/ML/DL algorithms and research results
* Contributor to our code repositories by developing code, reviewing colleagues’ code, debugging our systems etc##
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* About You:

*** Advanced degree (e.g. M.Sc., PhD) in a field related to AI/ML or Data Analytics such as Computer Science, Mathematics, Statistics, Physics, Biophysics, Computational Biology or Engineering Sciences
* 3+ years of industry experience with a track record of developing, deploying and applying ML/Deep Learning (DL) approaches to solve molecule-related problems
* Strong familiarity with advanced statistics, ML/DL techniques including various network architectures (CNNs, GANs, RNNs, Auto-Encoders, Transformers, LLM, PLM etc.), regularization, embeddings, loss-functions, optimization strategies, or reinforcement learning techniques
* Proficiency in Python and deep learning libraries such as PyTorch, Tensor Flow, Keras, Scikit-learn, Numpy, Matplotlib
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