Computational Biology team plays a key role in the analysis of multi-omic datasets, with a focus on single-cell RNA and DNA analysis, to support our research efforts to improve drug design in gene and cell therapy. Moreover, the computational biologist actively participates in the development of WLG’s In-silico tools, pipelines and models, notably for payload design and manufacturing process optimization. The Data Scientist will work under close supervision, receiving clear instructions and guidance from senior scientists, the Principal Scientist and the Head of Computational Biology.

Our Computational Biology Team plays a key role in the development of predicting and optimization tools for payload design and manufacturing in the context of gene and cell therapy. We perform analysis of multi-omic datasets, with a focus on single-cell RNA and DNA data, to support our research efforts to improve drug design in gene and cell therapy. Moreover, the computational biologist actively participates in the development of WLG’s In-silico tools, pipelines and models.

We are looking for highly skilled and collaborative individuals who are naturally curious, have a passion for learning and solving complex problems with a “can-do” mindset.

As a Data Scientist, here’s how you will make an impact: You will

• Design and run new models for Whitelab Genomics’ projects, with a particular focus on bioprocess optimization.

• Contribute to the development of new Machine Learning models implementations, features and applications of WhiteLab Genomics’ gene and cell therapy platform.

• Help in the development and knowledge of extraction from several databases.

• Support the development of biological data analysis pipelines.

• Collaborate closely with cross-functional teams.

We’re eager to meet you if you have

• Recent PhD or master’s in data science, computational biology, bioinformatics, biostatistics, or a related field

• 1–4 years of experience in Data Science and ML/DL in bioinformatics, biotech or biomedical context

• Proficient use of Python and bash and the standard machine learning libraries (scikit-learn, TensorFlow, PyTorch, etc…)

• Excellent communication skills, with the ability to effectively convey complex concepts to a non-technical audience

Nice to have

• Experience/knowledge in bioprocess optimization

• Experience with generative models (GANs, diffusion, etc…)

• Experience in omics data analysis

• Experience/knowledge in gene and cell therapy

• Experience in client-facing roles

• Docker, deployment pipelines, and software development skills

• Teams with Sara & Dina Z (comp-bio team)

• Teams with Dina L (People & Culture team)

• Interview in person (comp-bio team + P&C)