Computational Biologist

About

At WhiteLab Genomics, we believe that every patient deserves access to life-saving treatments. We won’t rest until that belief becomes a reality.

Driven by our commitment to transforming the future of healthcare, we’ve earned our place among the top innovators, including Y Combinator, French Tech 2030, and Future 40 by Station F. We’ve also been recognized by The Galien Foundation as a “Best Startup” in healthcare innovation and as Manufacturing Tech Disruptor of the Year - 2024 at Advanced Therapy Awards: Phacilitate in the USA, among other accolades.

We're committed to revolutionizing genomic medicine, delivering single-dose cures to millions with cancer and neurodegenerative and rare diseases worldwide.
By integrating cutting-edge data science, computational biology, and structural biology to design genomic medicine vectors with unprecedented precision, we're advancing the frontiers of healthcare and addressing critical challenges with the highest stakes.

If you’re passionate about improving patient outcomes and the intersection of biology and technology, we’d love to meet you!

Join us – together, we’ll bring life-saving treatments to the people who need them most.

Job Description

Our Computational Biology Team plays a key role in developing prediction and optimization tools for payload design and manufacturing for  gene and cell therapy. We analyze complex  multi-omic datasets, particulary single-cell RNA-seq and DNA –based assays , to support our research efforts to improve drug design in gene and cell therapy. The Computational Biologist will actively participate to the development of WLG's In-silico tools, pipelines and models.

As a Computational Biologist, here's how you will make an impact:

You will:

  • Provide bioinformatics analyses and contribute to the development of innovative approaches for various research and customer projects in collaboration with the other technical teams

  • Develop and implement algorithms and statistical methods for the integration, visualization, and interpretation of complex biological datasets, with a strong focus on multi-omic data

  • Participate and actively collaborate within cross-functional and cross-thematic projects, working under the supervision and guidance of project managers

  • Support the improvement, design and maintenance of in-house data analysis pipelines and contribute to the integration of new computational methodologies and best practices

  • Assist in the validation of machine learning solutions for vector and synthetic promoter designs, help advance WhiteLab Genomics’ internal R&D platforms

  • Contribute to hypothesis generation and experimental design in computational biology

  • Support internal research projects to advance core scientific methodologies, focusing on the development of methods leveraging multi-omic data to optimize the design of DNA sequences used in cell and gene therapies

  • Present your findings and discoveries with the WhiteLab Genomics teams, enhancing our collective knowledge and contributing to our overall success

Preferred Experience

We’re eager to meet you if you

  • Hold a recent PhD or Master’s degree in bioinformatics, computational biology, biostatistics, or a related field

  • Have 1–4 years of experience in biological data analysis

  • Possess strong programming skills in Python or R, with familiarity using bioinformatics toolkits and libraries (e.g. Bioconductor, scikit-learn, Scanpy, Seurat)

  • Are proficient in the analysis of multi-omic datasets, such as single-cell RNA-seq, ATAC-seq, and other NGS data types

  • Have a solid foundation in molecular biology and experience working with biological databases and reference resources (e.g., Ensembl, UniProt, NCBI).

  • Demonstrate a strong understanding of statistical methods for analyzing biological data

  • Can communicate complex scientific concepts clearly with internal and external stakeholders and work effectively in a collaborative research environment.

Nice to have

  • Proficient in workflow management systems (e.g., Nextflow, Snakemake, Terra, Airflow) and containerization technologies (e.g., Docker, Singularity).

  • Experience/knowledge in gene and cell therapy

  • Experience in machine learning, deep learning methods and tools (e.g. TensorFlow, Keras)

  • Experience working on cloud or HPC environments.

Recruitment Process

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

  • Teams with Dina L (People & Culture team)

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

Additional Information

  • Contract Type: Full-Time
  • Location: Paris
  • Education Level: PhD and more
  • Experience: > 2 years
  • Possible partial remote
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