AI/ML Scientist, Computational Biologist

AI/ML Scientist, Computational Biologist

W2 Contract

Pay Rate: $85 - $105 per hour

Location: Redwood City, CA - Remote Role

Job Summary:

We are seeking a hands-on Contract AI/ML Scientist to assess scientific data and analytical methods supporting downstream machine learning. The primary focus is computational biology and partnership with bioinformatics on affinity purification mass spectrometry (AP-MS), including profile similarity, technical reproducibility, and batch effects. This individual contributor will coordinate analytical requirements and ensure bioinformatics deliverables meet agreed modeling needs, with flexibility to support computational biology requirements for other projects.

Duties and 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.

Requirements and Qualifications:

• 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.

Preferred Qualifications:

• 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.

Desired Skills and Experience

Computational Biology, Bioinformatics, Machine Learning, Statistical Analysis, Biological Assay Data Analysis, Quantitative Proteomics, Affinity Purification Mass Spectrometry (AP-MS), High-Dimensional Biological Profiling, Profile Similarity Analysis, Technical Reproducibility, Experimental Controls, Measurement Uncertainty, Missing Data Assessment, Data Quality Evaluation, Plate and Batch Effect Analysis, Batch Correction, Confounding Assessment, Reference-Panel Pilot Design and Validation, Control-Relative Scoring, Data Normalization, Preprocessing Evaluation, Biological Signal Preservation, ML Dataset Readiness, Held-Out Evaluation, Information Leakage Prevention, Benchmark Design, Scientific Evidence Evaluation, Python or R, Git, Reproducible Analyses, Method Documentation, Data Provenance, Analytical Requirements and Acceptance Criteria, Milestone Management, Scientific Communication, Cross-Functional Collaboration, Independent Execution, Secure Data Handling, Drug Discovery, DEL Analysis, Databricks, Cloud Computing, High-Performance Computing, Biotechnology Research

Bayside Solutions, Inc. is not able to sponsor any candidates at this time. Additionally, candidates for this position must qualify as a W2 candidate.

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