Applied ML Scientist - Chemistry & Polymer Optimization

Jobtailor; Columbus (OH); On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor in Columbus, OH is seeking an experienced ML engineer to lead model development for prediction and optimization across chemistry, formulation, property, and process. You will collaborate with R&D and manufacturing to frame problems and drive data-driven solutions.

The role requires building and evaluating models (regression, trees, Gaussian processes, neural nets), curating chemical data, and quantifying performance and uncertainty to guide decision making.

Qualifications

• Bachelor’s degree in a related field as stated in the requirements.
• 5+ years of experience as required.
• Strong Python programming skills.
• Experience in applied statistics and uncertainty quantification.
• Domain knowledge in chemistry, preferably polymer chemistry.

Responsibilities

• Lead ML model development for prediction and optimization across chemistry, formulation, property, and process.
• Collaborate with R&D and manufacturing on problem framing.
• Curate and analyze chemical and experimental data.
• Develop and select input representations for raw materials, chemistry, and signal data.
• Select, train, and evaluate models across families such as regression, tree ensembles, Gaussian processes, neural networks.
• Quantify and calibrate model performance and uncertainty.

Skills

Python

Statistics

Uncertainty quantification

Chemistry knowledge

Polymer chemistry

Data analysis

Collaboration

Education

Bachelor’s degree in Computer Science, Data Science, Computational Chemistry, Cheminformatics, or related field

Job description

Jobtailor in Columbus, OH is seeking an experienced ML engineer to lead model development for prediction and optimization across chemistry, formulation, property, and process. You will collaborate with R&D and manufacturing to frame problems and drive data-driven solutions.

The role requires building and evaluating models (regression, trees, Gaussian processes, neural nets), curating chemical data, and quantifying performance and uncertainty to guide decision making.