Adaptyv Scientist, Binding Kinetics reviews binding data from automated lab assays and judges the quality of kinetic curves. They also communicate results and explanations directly to customers.
Responsibilities
Our lab produces binding data faster than any one person can look at it. Runs come off the Gators and the Carterra, go through our processing pipeline, and land in a review queue. Somebody with real judgment then has to decide: is this curve real? Is that a fit artifact or a genuine two-state binding event? Does this replicate get delivered, or does the sample get re-run? That person is you.
The second half of the job is talking to the customer about it. When a design team gets a plate back and three of their best predictions look like non-binders, they want to talk to someone who can explain why: reference subtraction, avidity, an off-rate at the edge of what the instrument resolves, or simply a bad protein. You'll be that person, on the call, sharing your screen, walking through sensorgrams.
This is a role for a scientist who is very good at kinetics and also likes people. If you like one but not the other, you'll be unhappy here.
Review the binding data coming out of the lab. Every SPR and BLI run passes through our processing pipeline and then to human review, and you're the expert eye on the output. You decide what gets delivered, what gets flagged, and what gets re-run.
Judge the curves, not just the numbers. A clean-looking Kd on a bad fit is worse than no number at all. You know the difference between a real off-rate and a drifting baseline, and you can say why.
Review data from our other assays too: thermostability, expression, developability, enzyme activity. Binding is the core of the role but it isn't all of it.
Talk to customers about their results. Deliverable walkthroughs, follow-up questions, and the awkward conversation when the data doesn't say what they hoped. You explain what we measured, how confident we are, and what to do next.
Turn review into experimental design. When something needs a re-run, you decide what changes: orientation, regeneration, concentration series, or a switch from BLI to SPR for the sensitivity.
Analyze at the batch level, not just the sample level. With hundreds of results per experiment, you should be writing a script to find the pattern rather than clicking through a queue. Systematic drift, a bad reference channel, or a target lot behaving differently than the last one shows up in aggregate long before it shows up in any single curve.
Feed what you learn back into the pipeline. When you catch a case where automation passed something it shouldn't have, or failed something it shouldn't have, that becomes a concrete requirement for the software team.
Work closely with production (what actually happened in the lab), QC (systematic quality), and customer success (the relationship around the science).
Qualification
MSc / PhD in biochemistryDeep SPR and BLI experienceReal fitting judgmentStrong data analysis skillsYou use AI tools seriouslyA self-starter who works independentlyA fast learnerStartup speedComfortable in front of customersAble to explain hard things simplyHonest about uncertainty
Required
MSc / PhD in biochemistry, biophysics, or a relevant field, plus 3+ years working hands-on with binding kinetics.
Deep SPR and BLI experience. Biacore, Carterra, Octet, Gator: the platform matters less than the fact that you've run these assays, troubleshot them, and interpreted a lot of data from them. You've seen enough sensorgrams to know immediately when one is lying to you.
Real fitting judgment. 1:1 vs. bivalent, when steady-state is appropriate and when it isn't, how mass transport and avidity distort a result. You can defend a Kd, or refuse to report one.
Strong data analysis skills. Python or R, enough to pull a few hundred results out of a database, fit them, plot them, and find the outliers. This role generates too much data to review by hand.
You use AI tools seriously. We run on Claude Code and similar tooling internally, and the people who get the most done here are the ones who reach for it by default: writing the analysis script, querying the database, building the one-off dashboard. You don't need to have done this before, but you need to want to. If your instinct is that real scientists do it by hand, this isn't the place.
A self-starter who works independently. Nobody is going to hand you a prioritized queue every morning. You'll figure out what matters most, go do it, and tell us what you found.
A fast learner. Our platform, our assays, and our software change constantly. You'll be handed unfamiliar tools and expected to be productive with them quickly.
Startup speed, not academic pace. Experiments ship to paying customers on deadlines. Getting a good answer this week beats a perfect one next quarter, and we'd rather flag an ambiguous result than sit on it.
Comfortable in front of customers. Many are PhD-level scientists at pharma companies and AI labs, and they will push back. You should enjoy that conversation rather than dread it.
Able to explain hard things simply. Some customers are biophysicists. Some are ML researchers who have never touched a pipette. You adjust without condescending to either.
Honest about uncertainty. When the data is ambiguous, you say so, to us and to the customer. We would much rather flag a weak result than ship a confident wrong one.
Familiarity with other biophysical or functional assays (DSF, HPLC, plate-based activity assays) is a plus.
