Data Scientist

Chicago Innovation Center | In office CIC Employees
Chicago, IL 60607, USA

• Travel Required :
  Yes

Description

We're looking for an experienced Data Scientist with a background in materials, chemistry, polymers, or chemical engineering to transform how data drives decisions across R&D and manufacturing. You'll build advanced statistical and machine learning models, develop digital twins, and accelerate our understanding of materials, chemistry, and processes through predictive modeling and optimization. This role sits at the intersection of scientific insight, statistical rigor, and real-world impact.

The ideal candidate pairs strong full‑stack data science skills with domain intuition and thrives in translating complex technical problems into practical solutions.

As a senior individual contributor, you'll lead end‑to‑end modeling initiatives and translate insights into deployed solutions and operational recommendations that improve yield, quality, cost, and cycle time. You'll work across diverse data environments (from small, high‑value R&D experiments to complex, high‑dimensional production datasets) turning complexity into clear, actionable direction. Your work will directly shape how teams access, use, and trust data, helping build a more agile, innovation‑focused organization.

Beyond building models, you'll help elevate our broader data science capabilities by developing reusable tools, scalable workflows, and high‑quality data assets that amplify impact across projects and teams. This is an opportunity to do meaningful, technically challenging work while shaping how data science is applied in a materials and manufacturing environment.

Key Responsibilities

Scientific and Statistical Partnership

• Partner with scientists, engineers, and manufacturing teams to frame high‑impact problems, assess data quality, and apply rigorous statistical thinking to materials, process, and production challenges.
• Bring a strong scientific lens to every analysis by ensuring methods are not only technically sound, but meaningful in the context of chemistry, materials behavior, and real‑world process dynamics.

Predictive Modeling, Digital Twins, and Optimization

• Design, build, and evolve advanced statistical and machine learning models that drive technical decision making across R&D and manufacturing.
• Work with domain experts to support development of digital twin and hybrid models that combine first‑principles knowledge with machine learning to simulate, predict, and optimize material and process performance.
• Own models through the full lifecycle ensuring they are robust, interpretable, and actionable in operational environments.

Data Transformation and Feature Engineering

• Work across complex, multi‑source datasets spanning laboratory, pilot, and manufacturing environments, transforming raw data into structured, analysis‑ready assets.
• Engineer meaningful features that unlock insight into structure‑property‑process‑performance relationships and improve model performance, interpretability, and usability.

Visualization, Communication, and Decision Support

• Translate complex analyses into clear, compelling visualizations, tools, and narratives that enable teams to quickly understand and act on insights.
• Deliver recommendations that directly influence R&D direction, process optimization, and manufacturing performance, and communicate effectively across diverse audiences.

Leadership, Capability Building & Data Advancement

• Lead data science initiatives from problem definition through sustained use in decision‑making, working across R&D and manufacturing.
• Act as a thought leader to technical teams by shaping analytical approaches, guiding best practices, and mentoring others in statistical thinking and disciplined use of data.
• Drive improvements in how technical data is structured, captured, and used and develop reusable tools, workflows, and codebases that scale impact beyond individual projects.

Qualifications

• Education
• Bachelor’s degree in Materials Science, Chemistry, Chemical Engineering, Polymer Science, Data Science, Statistics, Computer Science, or a related technical field;
  Master’s or PhD strongly preferred
• Experience
• 5+…