Bioinformatician

EpiSign Inc. is seeking a motivated Bioinformatician to support the development of DNA methylation data processing tools. Over the course of a 12-month project (with potential to extend), this individual will contribute to advancing EpiSign’s machine learning–based diagnostic platform by evaluating, validating, and optimizing methods for data processing and classifier training using multi-platform methylation datasets (microarray, PacBio, ONT, Illumina 5-base).

This position offers a unique opportunity to gain hands-on experience at the intersection of bioinformatics, neuroanalytics, and precision medicine, while working closely with leading researchers from the London Health Sciences Centre Research Institute.

• Implement and test computational methods for processing DNA methylation datasets from defined episignatures and baseline samples.
• Deploy machine learning methods (e.g. SVM, Random Forest, XGBoost) to validate classification performance.
• Perform data preprocessing, normalization, and quality control across multiple assay platforms (e.g., Illumina microarray, PacBio, ONT).
• Support development of open-source software tools (R/Python) for data processing and evaluation.
• Collaborate with EpiSign scientists and external partners to integrate findings with public and private datasets and workflows.
• Contribute to the preparation of manuscripts, methods tutorials, and knowledge translation materials.

• BSc or MSc in Bioinformatics, Computational Biology, Computer Science, Genomics, or related field.
• 1–3 years (junior) or 3–5 years (mid-level) relevant experience in biological data analysis, ideally with DNA methylation or other omics data.
• Strong programming skills in R and/or Python, with experience using data science and ML libraries (e.g., tidymodels, scikit-learn).
• Familiarity with biological data formats (BED, VCF, FASTQ, BAM) and common methylation analysis pipelines.
• Experience with Linux environments and cloud computing (e.g., AWS, GCP, or Azure).
• Understanding of statistical modelling, normalization, and batch correction techniques.
• Excellent communication skills and ability to document reproducible analytical workflows.