Applied ML Scientist - Active Learning

Lead the design and execution of optimization and active‑learning campaigns across chemistry, formulation, and process development Collaborate with R&D and manufacturing on framing optimization problems Design and coordinate sequential experiment campaigns using Bayesian optimization Drive closed‑loop optimization that connects surrogate models to experimentation Partner with ML engineers, software engineers, and process engineers to deploy and monitor optimization systems Communicate methods, results, and their limitations clearly Requirements Bachelor's degree in Computer Science, Data Science, Statistics, Operations Research, or a related field 7+ years experience Demonstrated expertise in Bayesian optimization and Gaussian processes Experience designing and running experiment campaigns or closed-loop optimization Strong Python skills and experience with mainstream Python‑based ML and Bayesian optimization frameworks and tools Active use of AI‑assisted coding and familiarity with emerging ML methods Strong communication, collaboration, and stakeholder management skills #J-18808-Ljbffr Jobtailor