Computational Biologist
Cambridge, United Kingdom
Technology Development /
Permanent, full-time /
Hybrid
Biofidelity is a revolutionary genomic technology company dedicated to unleashing the potential of genomics to transform human health and the world we live in. Our adaptive platform technology removes the noise from genomic data, providing only the information needed to make decision making less complex, faster, and more accurate.
At Biofidelity we are proud to have built a culture of humanity, openness and high expectations. We work towards well-defined goals, with a strong belief that we can make genomics globally accessible. Our people are keen to take responsibility for their work and share both their successes and challenges.
Biofidelity’s core values are Humanity- care & compassion; respecting personal lives; humour. Openness- sharing research; experimental approach; permission to fail. High expectations- be fearless; make a difference; willingness to do what’s needed. Our core values define our company culture, they are shared values, goals, attitudes and practices that characterise Biofidelity employees. We are looking for likeminded people to join the Biofidelity team.
Alongside a competitive salary and opportunities for career development, we offer a comprehensive benefits package including private medical & dental care, bonus, share option scheme, pension, income protection and life insurance, cycle to work scheme, season ticket loan, 26 days per year holiday allowance and free physical training sessions.
Biofidelity is seeking a talented and highly motivated Computational Biologist to join our Bioinformatics team. In this role you will play a pivotal role in developing and implementing statistical methods for our product development pipeline, from concept and development to clinical validation and product launch. You will work alongside colleagues from cross-functional teams. An ideal candidate is collaborative, adaptable and enjoys working in a dynamic environment. This role reports to the Director of Bioinformatics.
Key responsibilities:
● Develop and implement novel mathematical models and statistical analyses for variant calling and detection of minimal residual disease (MRD)
● Develop, implement and optimise machine learning methods for the analysis of NGS datasets and to improve design of hybridisation probes
● Work closely with the technology development team, providing insight into data that improves assay performance
● Maintain documentation and version control for developed software and pipelines, ensuring reproducibility and transparency of analyses.
Knowledge, Skills and Abilities:
● Strong background in mathematics, statistics, or computational biology (essential) and oncology (desired)
● Experience in the development of statistical and machine learning methods (for example, using TensorFlow, PyMC , PyTorch, sklearn and/or SciPy) and tools (essential)
● Prior experience of processing, analysis and interpretation of biological data (essential) or NGS data (desired)
● Scientific software development experience (essential, proficiency in one or more of Python (strongly preferred), C, C++, or R)
● Ability to work independently and take ownership of development of novel mathematical models and statistical analyses for variant calling and detection of MRD (essential)
● Clear communicator, with a ‘can do’ attitude and the ability to work across the company including non-technical colleagues (essential)
● Familiarity with standard software engineering tools (Jira, Git, CI/CD) (essential)
● Experience using bioinformatics workflow management systems (desired)
● Experience in architecting and developing cloud-based solutions in Amazon Web Services (desired)
● Knowledge of relational databases such as MySQL and PostgreSQL for managing and querying large datasets (desired)
Requisite Education and Experience / Minimum Qualifications:
● PhD in statistics/biostatistics/mathematics/computational biology/ bioinformatics or BSc/MSc in fields of computer science with previous relevant industry experience
● Experience in postdoctoral research or industry (essential)
