Data scientist/chemist (H/F)

Join our team as a Data Scientist/Chemist and be at the forefront of integrating artificial intelligence into chemical synthesis processes. You will design, develop, and deploy a Bayesian optimization system for mechanochemical processes, enabling real-time monitoring and automatic identification of optimal operating conditions. This interdisciplinary role requires expertise in mechanochemistry or chemistry, real-time analytical techniques, experimental design, data analysis, and programming. You will work closely with experimental chemists, monitor project progress, and contribute to scientific and technical reports.

• Concevoir, développer et déployer un système d'optimisation bayésienne pour un processus de synthèse chimique, en particulier pour les processus mécanochimiques.
• Intégrer et automatiser la collecte de données à partir de l'équipement de synthèse, établir une interface de communication entre les instruments expérimentaux et la plateforme de calcul.
• Travailler en étroite collaboration avec les chimistes expérimentaux/processus pour définir les paramètres pertinents et interpréter les résultats.

**Main skills:**

• Proficiency in programming languages such as C++, Python, and MATLAB
• Experience with Bayesian optimization libraries ( e.g., GPy, BoTorch, GPflow, PyMC3)
• Strong understanding of the principles of Bayesian optimization and machine learning
• Knowledge of experimental data acquisition and management systems (APIs, databases, communication protocols) applied to chemistry
• Solid skills in data analysis, data processing, modelling and interactive visualization (Plotly, Dash, Streamlit)
• Ability to work at the interface between data science and experimental chemistry
• A background in chemistry is a significant advantage for this position (e.g., knowledge of organic chemistry, reaction mechanisms, interpretation of experimental data by solution-based and solid-state characterization techniques, including PXRD, in-situ monitoring of mechanochemical processes by RAMAN spectroscopy).
• Knowledge of mechanochemistry is a plus

**Additional skills :**

• Basic knowledge of process chemistry and an understanding of experimental constraints would be highly valued
• Experience with version control tools and collaborative development environments (e.g., GitHub)
• Ability to document code and adhere to software development best practices (e.g., maintaining a project wiki)
• Ability to work effectively in an interdisciplinary and collaborative environment (international team)
• Proficiency in written and spoken English is a must.
• Proactive mindset and ability to take initiative

**Degree :**

PhD in process engineering, chemistry, data science, or a related field, with applications in optimization (particularly Bayesian optimization), machine learning, or process control. Candidates with additional expertise in programming, data science, real-time monitoring, machine learning or process automation are strongly encouraged to apply.

**Experience :**

• 2–3 years of experience (extended internships, postdoctoral fellowship, or first professional position) in modeling, programming, optimization, or data science applied to physico-chemical systems or industrial processes, synthetic chemistry and real-time in situ monitoring technics
• Proven experience in experimental optimization (Design of Experiments—DoE, sequential optimization, ideally Bayesian optimization) in a laboratory or pilot-scale environment
• Proven experience in in-situ monitoring techniques, elucidation of reaction mechanisms, kinetic modelling is a plus
• Involvement in laboratory and/or pilot scale Active Pharmaceutical Ingredients (API) development would be highly appreciated
• Experience working directly with experimental R&D teams (chemists, process engineers)
• Experience in data acquisition/management systems (databases, instrument APIs, ELNs, etc.)