Senior Machine Learning Scientist – Bioinformatics, Python, ML – USA, Remote

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:The primary priority is for the candidate to have publication‑backed experience, specifically in:Predictive ML on biological data (classification modeling)Genotype/feature → phenotype modelingMethod development or algorithmic contributionsModel interpretability and generation of biological insightTechnical ExpertiseStrong background in machine learning or statistical learning with substantial hands‑on experience developing classification modelsExperience working with high‑dimensional, sparse biological or omics datasetsStrong proficiency in Python for end‑to‑end machine‑learning workflowsDemonstrated experience designing validation strategies and assessing performance under significant class imbalance and limited sample sizesScientific RigorClear understanding of model limitations, uncertainty, and overfitting risks in real‑world biological datasetsExperience delivering machine‑learning analyses intended to inform research and internal decision‑makingExperience making principled methodological recommendations in the face of incomplete or noisy dataPreferred QualificationsExperience working with biological sequence data or genotype–phenotype analysesExperience with interpretability or explainability approaches applied to biological machine‑learning modelsBackground in pharmaceutical, biotech, or regulated research environmentsMachine Learning Model DevelopmentDevelop classification models to analyze curated genotype–phenotype datasetsApply appropriate modeling strategies to predict viral sensitivity or resistance based on sequence‑derived featuresImplement training, validation, and hyperparameter‑tuning workflows using predefined datasetsEvaluate alternative feature representations provided by the bioinformatics team and assess their suitabilityModel Evaluation and RobustnessAssess model performance using metrics appropriate for imbalanced biological datasetsEvaluate robustness across data splits, phenotype definitions, and successive data releasesIdentify failure modes, instability, and limitations, and document their implicationsDocument modeling assumptions, trade‑offs, uncertainty, and limitations in a reproducible and transparent mannerInterpretability and Insight GenerationProvide interpretable summaries of model behavior, including feature importance and consistency of signalsIdentify amino‑acid positions or features that recur across models or resampling strategies, while highlighting where signals are not reproducibleClearly document and communicate findings, assumptions, and caveats within the bioinformatics team