The role is the expert reviewer of binding data from automated SPR and BLI runs, deciding which results get delivered, flagged or re-run. It also means walking customers through their results, including disappointing ones, and turning review findings into better experiment designs. A strong candidate has several years of hands‑on kinetics work, sound judgment on curve fitting, and enough Python or R to analyze hundreds of results at once.How it fits the AI eraAI models can design proteins quickly, but someone still has to judge whether the physical measurement is trustworthy. Here, scripts and AI coding tools handle the repetitive sorting and pattern finding across large batches, while the scientist decides what a curve really means. Honest explanation of uncertainty to customers, and the call on whether a result is real, stay with people.How it builds AI experienceThe work sits right where AI-designed proteins meet real experimental data, so exposure to how AI teams test and refine their designs comes built in. Daily use of AI coding tools for analysis scripts and dashboards is expected, and spotting where the automated pipeline misjudged a result helps shape the software itself. Anyone short on this can start by practicing AI-assisted analysis of public binding datasets.Worth showingSPR and BLI troubleshootingDefending fit choicesPython or R batch analysisExplaining results to customersUsing AI coding tools dailyThis is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.About Adaptyv BioAn automated lab that tests AI-designed proteins, so researchers can send a sequence and get real experimental data back. #J-18808-Ljbffr
Scientist, Binding Kinetics
