Applied ML Scientist: Bayesian Optimization, Active Learning

Jobtailor; Columbus (OH); On-site; USD 120,000 - 160,000; Full time

Job summary

Jobtailor in Columbus, OH seeks a senior optimization lead to design and execute Bayesian optimization campaigns across R&D and manufacturing. You will frame problems, coordinate sequential experiments, and drive closed-loop systems that connect models to experiments.

The role requires deep Python expertise, experience with ML/BO frameworks, and strong communication to collaborate with cross-functional teams and stakeholders in a fast-paced environment.

Qualifications

• 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

Responsibilities

• 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

Skills

Python

Bayesian optimization

Gaussian processes

Experiment design

Communication

Education

Bachelor's degree in Computer Science/Data Science/Statistics/Operations Research

Job description

Jobtailor in Columbus, OH seeks a senior optimization lead to design and execute Bayesian optimization campaigns across R&D and manufacturing. You will frame problems, coordinate sequential experiments, and drive closed-loop systems that connect models to experiments.

The role requires deep Python expertise, experience with ML/BO frameworks, and strong communication to collaborate with cross-functional teams and stakeholders in a fast-paced environment.