Note: The job is a remote job and is open to candidates in USA. Bayside Solutions is recruiting for a contract AI/ML Scientist role; the hiring client is not identified. The role assesses scientific data and analytical methods for downstream machine learning, with a focus on computational biology and AP-MS data in partnership with bioinformatics.

Responsibilities

• Work with bioinformatics and team scientists to define analytical requirements, deliverables, milestones, and acceptance criteria for experimental data that will be used for machine learning
• Assess AP-MS profile similarity and technical reproducibility, considering quantitative support, missingness, controls, and uncertainty
• Evaluate plate and batch effects, distinguishing technical variation from biological differences and experimental confounding
• Support reference-panel pilot design and assess scoring and normalization approaches against agreed validation criteria for AP-MS and other data types
• Assess whether preprocessing preserves biological information and produces suitable inputs for downstream modeling
• Build reproducible analyses and document methods, assumptions, limitations, and provenance
• Support scientific data evaluation and benchmark design as needed
• Communicate findings and unresolved questions, coordinate follow-up with bioinformatics, and align analytical work with team needs

Skills

• Degree in computational biology, bioinformatics, statistics, quantitative biological science, or a related field, or equivalent research experience
• Demonstrated experience analyzing biological assay data and translating scientific requirements into practical analytical workplans
• Strong statistical judgment in assessing reproducibility, experimental controls, batch effects, and measurement uncertainty
• Proficiency in Python or R, with experience building reproducible analyses, maintaining code in Git, and documenting methods
• Experience assessing data quality, missingness, preprocessing assumptions, and the suitability of datasets for downstream analysis
• Working knowledge of machine learning validation, including held-out evaluation, confounding, and risks of information leakage
• Ability to explain uncertainty and analytical tradeoffs clearly and collaborate effectively with bioinformaticians, chemists, and biologists
• Ability to independently execute defined work, meet agreed milestones, incorporate feedback, and handle internal data securely
• Candidates for this position must qualify as a W2 candidate
• Advanced degree in computational biology, bioinformatics, statistics, or a related field
• Experience with quantitative proteomics, AP-MS, or other high-dimensional biological profiling assays
• Experience comparing profile similarity, control-relative scoring, batch correction, or reference-panel validation methods
• Experience with drug discovery, DEL analysis, scientific evidence evaluation, or collaboration between bioinformatics and ML teams
• Experience with Databricks, cloud/HPC workflows, or computational projects in biotechnology

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