Data Scientist (TE-DPS-AIM-2026-75-GRAP) at CERN

Geneva, Geneva, Switzerland -

Full Time

Start Date

Immediate

Expiry Date

28 Jun, 26

Salary

7004.0

Posted On

30 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, NumPy, Pandas, Scikit-learn, PyTorch, Time Series Analysis, Anomaly Detection, Predictive Modelling, Feature Engineering, Exploratory Data Analysis, Apache Spark, MLflow, REST APIs, Podman, Kubernetes, Git

Industry

Research Services

Description

Job Description Do you want to apply your Data Science and AI expertise to one of the world's most complex scientific and engineering environments? Join CERN and take a leading role in developing an AI driven prescriptive maintenance and operational assistance platform for the Large Hadron Collider (LHC). In this position, you will drive the full lifecycle of an innovative AI solution designed to improve the reliability and performance of accelerator systems. Your work will span requirements gathering, data preparation, model development and lifecycle management, integration of safety considerations, creation of AI-assisted diagnostics tools, and the implementation of robust data and API pipelines. You will develop a proof of concept using LHC Run 3 data, then lead testing and validation on representative systems within the TE Department, with the goal of achieving full deployment for Run 4. Your contribution will enable CERN to harness advanced AI methods to reduce unplanned downtime, strengthen operational resilience, and support data-driven decision making across critical accelerator infrastructure. Your responsibilities Collect and formalise operational use cases and user stories with engineers and maintenance experts; translate them into functional and technical specifications. Define system requirements and draft solution architecture for an AI-driven prescriptive maintenance platform. Design and implement scalable data pipelines for ingesting and processing operational data (e.g. time series, logs, equipment metadata, technical documentation) using distributed processing frameworks. Perform data pre-processing, feature engineering, and exploratory analysis to prepare datasets for modelling. Design and execute machine learning experiments for anomaly detection, failure prediction, and prescriptive recommendations. Implement experiment tracking, model validation, and reproducibility practices using ML lifecycle management tools (e.g. MLflow or similar). Package and deploy models into production environments through APIs and containerised services (e.g. Podman), supporting orchestration platforms such as Kubernetes. Contribute to system integration, testing, performance monitoring, and iterative improvement of deployed models. Produce clear technical documentation, specifications, and user-oriented material to ensure maintainability and knowledge transfer. This role includes team supervision responsibilities. Your profile Academic background in Data Science, Computer Science, Engineering, Applied Mathematics, or a related quantitative field. Experience working on applied machine learning or data-driven projects addressing real-world operational or engineering problems. Experience with version control systems (e.g. Git) and collaborative development workflows. Skills Proficiency in Python for data analysis and machine learning (e.g. NumPy, pandas, scikit-learn, PyTorch or similar frameworks). Understanding of machine learning techniques relevant to time series analysis, anomaly detection, and predictive modelling. Knowledge of data pre-processing, feature engineering, and exploratory data analysis (EDA). Familiarity with distributed data processing tools (e.g. Apache Spark) or similar big data frameworks. Good knowledge of experiment tracking and ML lifecycle management tools (e.g. MLflow or equivalent). Understanding of REST APIs and model serving concepts. Familiarity with containerisation technologies (e.g. Podman) and basic concepts of orchestration platforms (e.g. Kubernetes). Ability to write structured technical documentation and maintain reproducible code. Ability to structure complex, ambiguous problems into well-defined analytical approaches. Spoken and written English, with a commitment to learn French. Eligibility criteria: You are a national of a CERN Member or Associate Member State. You have a professional background in Data Science, Computer Science, Mathematics (or a related field) and have either: a Master's degree with 2 to 6 years of post-graduation professional experience; or a PhD with no more than 3 years of post-graduation professional experience. You have never had a CERN fellow or graduate contract before. Additional Information Job closing date: 20.04.2026 at 23:59 CEST. Contract duration: 24 months, with a possible extension up to 36 months maximum. Working hours: 40 hours per week Job flexibility: Fully Onsite Target start date: 01-June-2026 Job reference: TE-DPS-AIM-2026-75-GRAP Field of work: Data Science & Data Analytics Benchmark job: 200020 - Computing Engineer Global Benefits A monthly stipend between 6372-7004 Swiss Francs per month (tax free) depending on your degree. 30 days of paid leave per year plus 2 weeks annual closure. Coverage by CERN’s comprehensive health insurance scheme (for yourself, your spouse and children), and membership of the CERN Pension Fund. Family, child and infant monthly allowances depending on your individual circumstances. A relocation package (installation grant and travel expenses) depending on your individual circumstances. Possibility to extend your contract up to 36 months. On-the-job and formal training including language classes. Overview of CERN - Discover a world where the impossible is made possible! At CERN, the European Organization for Nuclear Research, we are pushing the frontiers of science and technology. Our groundbreaking work brings together not only physicists but also a diverse range of professionals from engineering, technical, scientific, and administrative fields. Together, we foster an environment where innovation and collaboration thrive. Every day, we face exciting new challenges and opportunities to contribute to cutting-edge research that shapes our understanding of the universe. We meet these challenges through the diverse perspectives within our teams, ensuring every contribution is valued and driving our shared sense of inclusion and purpose. Diversity is a core value of CERN since its foundation, and it remains central to our mission and continued success. If you are ready to be part of a dynamic, inclusive community pushing the boundaries of knowledge, CERN is the place where your curiosity and skills can thrive. Be part of our mission to uncover what lies at the heart of the universe! TAKE PART! More information about us, here: careers.cern

Responsibilities

The role involves driving the full lifecycle of an AI solution for prescriptive maintenance on the Large Hadron Collider, spanning requirements gathering, data pipeline creation, model development, and deployment into production systems. Responsibilities include defining system architecture, designing scalable data pipelines for operational data, executing machine learning experiments, and supervising a team.