Data Scientist / Research Developer – DME

Overview

The Data Scientist / Research Developer will join the DME team , a transversal group working closely with Protein Vector Engineering and Computational Biology . The role is primarily focused on data science and coding (Python).

Key Responsibilities

• Implementing and evaluating ML models
• Turning prototypes into clean, reusable code
• Helping to make data and models FAIR and systematic
• Work on data science tasks for internal projects related to gene and cell therapies in collaboration with other data scientists and domain teams
• Implement and maintain ML experiments reproducibly: data preprocessing, model training scripts, evaluation workflows
• Help transform research prototypes from notebooks into clean Python modules, small internal libraries or scripts reusable across projects
• Contribute to the FAIR and systematisation work of the team by adding tests, logging and configuration to existing code, refactoring models for reusability, and helping to standardise input/output formats
• Participate in benchmarking and comparisons of different models/approaches by setting up evaluation pipelines, tracking metrics, generating simple reports and visualisations
• Collaborate with senior team members to implement ideas from papers or internal proposals, iterate quickly on model and data improvements

Qualifications

MSc with 1–2+ years of experience (internships can count if substantial). PhD is a plus but not required.

Fields or equivalent experience: Data Science, Computer Science, Applied Mathematics, Statistics, Machine Learning, or related quantitative disciplines.

Preferred Experience

• End-to-end data science projects: data cleaning, feature engineering, model training, evaluation and reporting
• ML (classical and basic deep learning): regression, classification, tree-based models, basic neural networks; graph neural networks are a plus
• Software development and scientific pipeline development: writing modular Python code, packaging utilities, working with Git and Docker
• Working with real-world, messy data in any domain
• Biology / omics / healthcare data (a plus but not mandatory)