Job Description

Roles & Responsibilities

You will join a leading global R&D environment where advanced AI-driven applications support researchers in their daily work from data analysis and statistical modelling to predictive insights and dashboarding. These applications, built mainly with Shiny (R) and Streamlit (Python) , are part of a growing analytics ecosystem running on a dedicated internal platform. The mission combines maintenance of existing AI/statistical tools and development of new data-driven applications based on real user needs. You will act as a bridge between business users (researchers) and technical teams , ensuring applications remain reliable, scalable, and relevant. What you will do Investigate and fix issues in AI algorithms (bugs, performance, data pipeline failures) Maintain and improve existing Shiny (R) and Streamlit (Python) applications Ensure stability after platform or dependency updates Develop new interactive modules (dashboards, analytics tools, reporting apps) Analyse business needs and translate them into technical solutions Optimize and refactor Python and R codebases Manage data pipelines and connectors (APIs, databases, files) Conduct testing (UAT, unit tests, integration tests) Collaborate with data scientists, statisticians, and business users Document all developments and processes (French & English)

Desired Candidate Profile

• 3 5 years of experience in Python and/or R development
• Experience with Shiny (R) and/or Streamlit (Python)
• Strong debugging and problem-solving skills
• Experience maintaining production-level applications
• Solid knowledge of: Data manipulation (Pandas / dplyr) Data visualisation (ggplot2, plotly, matplotlib, altair)
• Experience with Git/GitHub (PRs, branching, versioning)
• Understanding of basic AI/ML concepts (evaluation metrics, overfitting, pipelines)
• Ability to work in a bilingual environment (French & English)
• Relocation to M laga, Spain
• Nice to have: Experience with GCP (BigQuery, Vertex AI)
• Knowledge of Docker and APIs (FastAPI, REST)
• Exposure to ML frameworks (scikit-learn, TensorFlow, PyTorch)
• Statistical knowledge (PCA, hypothesis testing, DOE)
• Experience with Posit / Shiny Server environments
• Familiarity with NLP or computer vision concepts