Applied Research Scientist (Computational Biology), Lausanne

Application Scientist (Computational Biology), Lausanne

Isomorphic Labs is a new Alphabet company that is reimagining drug discovery through a computational- and AI-first approach.

We are on a mission to accelerate the speed, increase the efficacy and lower the cost of drug discovery. You'll be working at the cutting edge of the new era of 'digital biology' to deliver a transformative social impact for the benefit of millions of people.

Come and be part of a multi-disciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring, collaborative and entrepreneurial culture.

Your impact

This is an exciting opportunity for you to contribute to an ambitious Computational Biology research programme, working in partnership with leading ML researchers, Chemists and Biological Scientists. Building on the successful models in place to predict protein structure (AlphaFold-latest), there is an unique opportunity for an applied Research Scientist to have a direct impact on drug discovery, using innovative Computational Biology approaches. This is a newly created role; driven by a passion for problem solving, you will need to use your previous experience and show initiative in order to fully carve out your contribution.

What you will do

• Partner with Isomorphic Labs Drug Discovery teams and wider organisation, to use pioneering Computational Biology methods to drive data enabled drug development
• Develop and apply Computational Biology workflows to address common drug development research questions, spanning from target identification to clinical biomarker strategies
• Use your shown experience of computational biology datasets, spanning statistical genetics, genomics, transcriptomics, proteomics, functional perturbation screens, imaging, knowledge graphs, PPI, clinical and other data types, to devise relevant computational experiments
• Use data to drive biological insights and contribute to drug discovery programmes, becoming an integral member of project teams
• Work in partnership with the Computational Biology research team members to evaluate experimental models under development and benchmark to existing methodologies
• Work with Bioinformatics, Data and other groups to influence Iso’s datasets and pipelines strategy, ensuring innovative insights are consistently brought to bear within drug development programmes
• Perform thorough data analysis and data quality assurance checks, with a strong focus on accuracy and reproducibility, inline with industry standard processes.
• Contribute to the wider development of Iso’s ML computational platform, working with other engineers to architect, build and operate the platform’s components
• Work with other members of the Computational Biology team to deliver a unified team strategy
• Provide documentation, guidance and communication on computational biology to the wider organisation.

Skills and qualifications

Essential:

• Experience in computational biology, with PhD and/or extensive experience
• Expertise with detailed data quality control procedures and data visualisation
• Solid experience with experimental design and statistical analysis
• Solid understanding of computational biology tools and methodologies and experience with the analysis of large -omics datasets
• Demonstrated understanding of the principles of molecular cell biology and genetics, or related biological disciplines
• Familiarity with data processing pipelines and tools
• Track record of using computational methods to gain novel biological insight
• Ability to effectively communicate scientific concepts to a variety of audiences
• Programming skills in a language such as Python (ideally), or R
• Experience using common database platforms (e.g. BigQuery), and data integration approaches
• Some exposure to cloud environments
• Demonstrate ongoing career progression / trajectory and a passion for learning

Nice to have:

• Post PhD research experience (i.e. some postdoctoral or industry experience)
• Prior experience in the context of therapeutic or diagnostic development programmes
• Familiarity with a variety of assaying techniques, including NGS, cell-based assays, functional genomics, single-cell techniques, and image-based assays with expertise in their respective data analysis approaches
• Experience working with clinical data
• Expertise in applying computational biology methods to the process of drug discovery, such as methods used for disease modelling and target discovery, combination strategies, as well as biomarker development.
• Experience applying computational biology workflows on GCP
• Strong experience in Python
• Hands-on experience in applying ML (especially deep learning) models in the field of computational biology.