Postdoctoral Researcher, Experimental Data Generation & CRO Strategy

Responsibilities: Design scalable campaigns for biomolecular interactions, conformational dynamics and related protein measurements. Select systems, constructs, assays and controls based on scientific value, feasibility, diversity, throughput and cost. Anticipate bottlenecks and define success criteria, contingency plans and follow-up experiments. Translate research goals into clear work packages, milestones and experimental requirements. Coordinate parallel programs with CROs and academic collaborators, review progress and guide corrective iterations. Provide scientific direction on protein production, assay development and biophysical or structural characterization. Review raw and processed experimental outputs, including binding curves and kinetic measurements. Diagnose artifacts, failed fits and systematic assay problems using quantitative and biophysical reasoning. Define reproducible QC criteria and scalable triage processes beyond manual review. Convert heterogeneous experimental outputs into traceable, model-ready datasets with appropriate metadata and provenance. Work with computational researchers to prioritize systems, evaluate model predictions and design informative follow-up experiments. Use basic scripting and data-analysis tools to organize, inspect and summarize experimental datasets. Communicate experimental findings, limitations and risks to biological and computational collaborators. Drive projects from ambiguous questions to usable datasets, scientific conclusions and publications. Contribute to the experimental data strategy for future BioEmu models.

Qualifications: PhD in Biology, Biophysics, Biochemistry, Molecular Biology, Protein Science, Bioengineering, Computational Biology, Molecular Modelling, or a related field, with experience designing, conducting, or analyzing biomolecular experiments and/or computational studies. Strong quantitative understanding of experimental measurements and their limitations. Ability to coordinate complex projects and communicate clearly across experimental and computational teams. Experience working with real-world biological, structural or biophysical datasets. Ability to independently own and deliver research projects. Experience managing CROs, vendors or distributed experimental collaborations. Expertise in protein-protein interactions, binder design, affinity optimization or high-throughput assay development. Familiar with techniques such as protein expression and purification, binding assays (SPR, BLI, ITC, cryo-EM), structural biology (X-ray crystallography, NMR), Mass Spec (HDX-MS, Cross-link Mass Spec).Practical Python or equivalent scripting skills for data analysis, QC and workflow automation. Experience in designing, curating or standardizing datasets for machine-learning applications. Interest in model-guided experimental design, drug discovery or therapeutic applications. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances.