​​​​​​​About PairX Bio

PairX Bio is a precision oncology company developing next generation therapeutics targeting cancer-associated antigens derived from alternatively spliced proteins. The PairX Bio discovery platform is built upon a deep understanding of mRNA splicing biology and robust validation of identified targets. The technology was developed and spun out from Professor David Epstein’s lab at Duke-NUS Medical School.

Job Description

Join our innovative and exciting oncology startup as a Bioinformatician, where you'll gain exposure to the early-stage biotech environment, develop your critical thinking, and participate in making informed decisions about target selection. Leveraging your expertise in bioinformatics and computational biology you will collaborate closely with our team of scientists and play a pivotal role in analysing and interpreting complex transcriptomic and molecular data to contribute to the identification of novel therapeutic targets and the development of cutting-edge treatments.

Responsibilities

1. Develop statistical and machine learning methods to identify therapeutic targets.
2. Contribute to development of data analysis workflows for NGS data, ensuring reproducibility and scalability of analyses.
3. Communicate and work with the discovery team to design experiments and validate findings.
4. Document analysis workflows and their associated data flow into SOPs, work instructions, protocols, and other technical documentation.
5. Manage and organise multiple target programs concurrently.
6. Present findings and insights to both technical and non-technical audiences, including team members, management, and potential collaborators.

Job Requirements

1. Ph.D. in Computational Biology, Bioinformatics, Biostatistics, or a related field, with experience in analysis of large-scale omics data sets (NGS data).
2. Strong programming skills in R, Python, and/or other languages commonly used in bioinformatics and data sciences.
3. Proficiency with Unix-based systems and running or scripting applications from the command line.
4. Familiarity with public genomics databases/resources and data processing
5. Knowledge of statistical analysis, machine learning, and data visualisation techniques applied to biological data.
6. Knowledge of cloud computing environments (especially AWS), server maintenance and upkeep.
7. Previous experience in the oncology space and downstream molecular biology analysis is a plus.
8. Experience in RNA splicing analysis is highly desirable.
9. Strong communication skills and ability to collaborate effectively in a multidisciplinary team environment.