Research Scientist, Structural Bioinformatics

This role is with Hummingbird Bioscience, a startup supported by SGInnovate.

Hummingbird Bioscience is an innovative clinical-stage biotech company focused on developing precision therapies against hard-to-drug targets to improve treatment outcomes. We harness the latest advances in systems biology and data science to better understand and solve the underlying causes of disease and guide development of our therapeutics.

Enabled by our proprietary Rational Antibody Discovery platform, we discover antibodies against optimal yet elusive epitopes on important targets that have not been successfully drugged, unlocking novel mechanisms of action. We are advancing a rich pipeline of first
- and best-in-class precision therapies in oncology and autoimmunity, in collaboration with global partners in academia and industry.

Our highly experienced teams in the US and Singapore span antibody discovery, pharmacology, production and clinical development. Together we aim to accelerate the journey of new drugs from concept to clinical care.

Develop and extend the capabilities of the mAbPredict platform

Develop and extend user-friendly interfaces to mAbPredict

Perform epitope selection for discovery campaigns

Development of the company’s structural bioinformatics pipelines

Develop and implement new approaches to in-silico antibody discovery

Where necessary manage in-house HPC and cloud-based bioinformatics software infrastructure

Improve existing proprietary code-bases and develop new bioinformatics tools and algorithms

Implement reproducible scientific software environments and pipelines

Train company staff in relevant bioinformatics techniques and/or deploy bioinformatics solutions to meet their needs

Provide support, mentorship and training to others

Establishment and maintenance of documentation procedures and configuration profiles

Perform other job-related duties as assigned

CORE EXPERIENCE AND QUALIFICATIONS

A relevant post-graduate degree in computer science, chemistry physics, mathematics, engineering, biology, biochemistry or similar

A minimum of 3 years post-PhD experience with demonstrated track record in project management

Open source contributions or publications in academic journals demonstrating experience in scientific software development

Good knowledge of antibody and/or protein-based modelling techniques

Good knowledge of machine learning as applied to protein structure

Good knowledge of Unix style operating systems and HPC clusters

Good knowledge of relational databases

Proficient coding skills in relevant languages such as Python

Demonstrated ability to use commonly used molecular dynamics packages (e.g. GROMACS, OpenMM etc) for protein-based simulation

Demonstrated ability to use commonly used homology modelling toolsets (e.g. Rosetta)

Good time management skills with the ability to effectively plan, prioritize and co-ordinate multiple tasks and adjust to changing priorities to deliver results to tight deadlines

Ability to find innovative solutions to complex problems and proactively seek out continuous improvement initiatives

Excellent interpersonal skills with the ability to build strong relationships across internal and external partners

Ability to work independently and with the flexibility to handle workflow in a fast-paced start-up environment

Excellent verbal and written communication skills with good attention to detail

IDEAL EXPERIENCE AND QUALIFICATIONS

A strong background in physical sciences is preferred

Use of machine learning in a variety of contexts (e.g. structure prediction, single-cell analysis)

Familiarity with AlphaFold and other deep learning approaches

Experience with in silico antibody design/engineering

Good understanding of cancer biology and immunology

Good knowledge of containerization and modern workflow management systems such as Docker, Snakemake, Nextflow or similar