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Drug Discovery Scientist

DeepLife is hiring!

About

DeepLife is a pre-series A startup focused on addressing the urgent need to increase drug discovery reliability by acting on the earliest step, drug target identification. This consists of identifying, a molecular target, such as a protein, that will trigger the transition from disease to healthy cells. With current methods, only 1 target in 10,000 reach the market, leading to a significant loss of time and efforts in the community.

Our approach is to leverage the recent revolution in the omics data, measuring precisely cells activity at large scale, and build foundation models to mimic cell behavior in various contexts and identify the optimal trigger to reverse disease state.

Half of the team today is dedicated to build the largest omics database, aka omics atlas, to map all human body tissues and diseases and reduce experimental biases.

We offer a research friendly environment, with 90% of the company holding a PhD, with academic collaborations and publications. The team is international and composed of +10 different nationalities. The company is remote first with most of the work is remote and regular events organized in our offices in Paris.

Job Description

Overview:

We are seeking a Drug Discovery Scientist with strong expertise in deep learning, systems biology, and computational biology, particularly in single-cell analysis. This role will focus on developing innovative methods for target identification, drug repurposing, and patient stratification to advance our drug discovery programs. The ideal candidate will have experience discussing with pharmaceutical companies and a solid understanding of the drug discovery pipeline.

Key Responsibilities:

• Method Development for Target Identification: Develop advanced computational methods using deep learning and systems biology to identify novel drug targets.

• Computational Approaches for Drug Repurposing: Create predictive models to discover new uses for existing drugs by analyzing multi-omics and single-cell data.

• Patient Stratification Techniques: Develop methods for stratifying patients based on molecular and cellular profiles using single-cell analysis.

• Knowledge Graph Construction and Ontology Management: Design and implement knowledge graphs using ontologies to enhance data integration, interpretation, and validation using large language models (LLMs).

• Interdisciplinary Collaboration: Work with biologists, chemists, clinicians, and data scientists to apply computational findings in practical settings.

• Continuous Learning and Innovation: Stay updated with the latest advancements in computational biology, deep learning, and systems biology.

• Communication: Present complex data and concepts clearly to diverse scientific and non-scientific audiences.

Preferred Experience

Qualifications (Ranked from Most Important to Least Important):

  1. Ph.D. in Computational Biology, Bioinformatics, Systems Biology, or a related field.

  2. Expertise in developing computational methods for target identification using deep learning and systems biology.

  3. Strong background in deep learning, preferably with experience in foundation models.

  4. Advanced knowledge in constructing and managing knowledge graphs with ontologies and validating data integration through LLMs.

  5. Experience in creating innovative computational approaches for drug repurposing.

  6. Proficiency in systems biology for integrating multi-omics data and modeling biological systems.

  7. Extensive experience in computational biology, particularly in single-cell analysis.

  8. Proficiency in programming languages such as Python or R and experience with bioinformatics tools and libraries.

  9. Proven ability to develop methods for patient stratification using single-cell analysis and computational biology.

  10. Experience working in interdisciplinary teams.

Additional Information

  • Contract Type: Full-Time
  • Location: Paris
  • Possible full remote
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