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Bioinformatics Lead - Cancer Genomics

Outsource SA Solutions
1 day ago
Full-time
Remote
South Africa
Web Development

Introduction

We are evaluating advanced AI computational models on end-to-end oncology research and clinical decision tasks. In this role, you will curate complex biological data into standardized, deterministically-graded benchmark tasks to assess model reasoning across translational cancer biology.

You will design evaluation frameworks that test AI model capabilities on complex research workflows. Your primary focus will be establishing robust reference standards and objective scoring frameworks, ensuring models are tested against realistic biological variability, incomplete datasets, conflicting evidence, and clinical trade-offs.

Duties & Responsibilities

  • Benchmark Development: Crate and curate real-world multi-omic cancer datasets to build deterministically-graded evaluation tasks for AI models.

  • Reference Standards: Establish ground-truth analysis standards and objective scoring systems for diverse clinical and biological outcomes (such as disease classification, target identification, and therapeutic actionability).

  • Failure-Mode Analysis: Stress-test AI models against edge cases, evaluating performance when faced with experimental noise, missing data, and conflicting literature evidence.

  • Multi-Omic Data Integration: Analyze complex datasets—spanning bulk and single-cell RNA-seq, somatic/germline variants, structural variants, and tumor-microenvironment profiles—to model biological pathways.

  • Standard Operating Procedures: Apply standard oncology classification rules and variant grading systems to score automated decisions against established clinical protocols.

  • Technical Documentation: Document reference pipelines, account for analytical uncertainties, and articulate alternative biological interpretations to continuously optimize benchmark quality.

Desired Experience & Qualification

Education: PhD, Master’s degree, or equivalent experience in Bioinformatics, Computational Biology, Cancer Biology, Genomics, or a related quantitative field.

 

Professional Experience: 1+ years of hands-on experience in translational, clinical, or academic cancer research.

 

Domain Expertise:

  • Hands-on experience analyzing multi-omic cancer data.

  • Proficiency in somatic and germline variant interpretation (WES/WGS/panels), copy number analysis, mutational signatures, or single-cell transcriptomics.

  • Exposure to tumor-immune profiling, spatial transcriptomics, or functional genomics.

 

Technical Skills:

  • Advanced proficiency in Python or R for computational biology.

  • Experience with standard bioinformatic tools for variant annotation, single-cell analysis, gene-set enrichment, and immune cell deconvolution.

  • Solid grounding in statistical analysis.

 

Framework Knowledge:

  • Understanding of standard clinical variant classification guidelines and therapeutic actionability frameworks.

  • General familiarity with published molecular subtyping frameworks and immune signatures

 

Preferred:

  • Prior experience constructing evaluation benchmarks, deterministic grading systems, or testing AI/ML models.

  • Familiarity with major public cancer genomics repositories and databases.

Package & Remuneration

TBD

Interested?