A large global organization are seeking a Senior Machine Learning Scientist to develop and evaluate internal predictive models linking viral genotype and phenotype data based sensitivity analyses. The role focuses on applying established machine‑learning approaches to curated biological datasets to support research, validation, and internal decision‑making.
This role will be an initial 6-8 months+ contract with the possibility of extensions.
The role can be worked on a remote basis.
Key Skills/Responsibilities:
Technical Expertise
* Strong background in machine learning or statistical learning with substantial hands‑on experience developing classification models
* Experience working with high‑dimensional, sparse biological or omics datasets
* Strong proficiency in Python for end‑to‑end machine‑learning workflows
* Demonstrated experience designing validation strategies and assessing performance under significant class imbalance and limited sample sizes
Scientific Rigor
* Clear understanding of model limitations, uncertainty, and overfitting risks in real‑world biological datasets
* Experience delivering machine‑learning analyses intended to inform research and internal decision‑making
* Experience making principled methodological recommendations in the face of incomplete or noisy data
Preferred Qualifications
* Experience working with biological sequence data or genotype–phenotype analyses
* Experience with interpretability or explainability approaches applied to biological machine‑learning models
* Background in pharmaceutical, biotech, or regulated research environments
Machine Learning Model Development
* Develop classification models to analyze curated genotype–phenotype datasets
* Apply appropriate modeling strategies to predict viral sensitivity or resistance based on sequence‑derived features
* Implement training, validation, and hyperparameter‑tuning workflows using predefined datasets
* Evaluate alternative feature representations provided by the bioinformatics team and assess their suitability
Model Evaluation and Robustness
* Assess model performance using metrics appropriate for imbalanced biological datasets
* Evaluate robustness across data splits, phenotype definitions, and successive data releases
* Identify failure modes, instability, and limitations, and document their implications
* Document modeling assumptions, trade‑offs, uncertainty, and limitations in a reproducible and transparent manner
Interpretability and Insight Generation
* Provide interpretable summaries of model behavior, including feature importance and consistency of signals
* Identify amino‑acid positions or features that recur across models or resampling strategies, while highlighting where signals are not reproducible
* Clearly document and communicate findings, assumptions, and caveats within the bioinformatics team