Data Scientist

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.

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., * 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+ years of applying data science, statistics, or advanced analytics to complex problems in materials, chemistry, manufacturing, or related technical environments

• Track record of delivering measurable impact through modeling and analysis (e.g. improvements in yield, quality, cost, or efficiency) and owning delivery from problem framing through deployment

• Experience with modeling across data scales and structures spanning small, high-value experimental datasets to large high-dimensional production or process datasets

• Experience working with manufacturing, process, or production systems, and connecting analysis to real-world operational performance

• Technical Skills

• Strong proficiency in Python and modern data science tooling (e.g., pandas, NumPy, scikit-learn, PyTorch/TensorFlow, LightGBM, SHAP); familiarity with R or JMP is a plus

• Deep grounding in statistical methods, including both frequentist and Bayesian approaches, with the ability to design experiments, quantify uncertainty, and make decisions under limited data

• Experience developing predictive and explanatory models, including feature engineering, latent variable methods, and interpretable modeling approaches

• Strong SQL skills and experience working with structured and relational data; familiarity with cloud-based data and analytics platforms (e.g., AWS, Azure)

• Experience creating interactive dashboards or data applications to support decision making (e.g., Power BI, Tableau, Streamlit, or Plotly Dash)

• Experience building and deploying models in production or operational environments, including version control (Git), reproducibility, and lifecycle management practices

• Experience with digital twins, hybrid modeling approaches, or combining physics-based understanding (preferred) with data-driven techniques for prediction and optimization

• Familiarity with generative AI techniques, large language models (LLMs), or retrieval-augmented generation (RAG) as applied to scientific or engineering workflows (preferred)

• Familiarity with materials modeling data or tools (e.g., DFT, MD, CALPHAD) or adjacent scientific computing approaches (preferred)

• Who you are

• Strong communicator who can engage effectively with scientists, engineers, manufacturing teams, and leadership and translates complexity into clarity

• Comfortable operating in ambiguous, cross-functional environments and taking ownership of high-impact problems without waiting for direction

• Self-directed senior IC who leads through influence by shaping analytical approaches, driving alignment across teams, and raising the bar for how data is used

• Energized by continuous learning and staying at the forefront of materials informatics, AI/ML, and scientific computing

2.92.9 out of 5 stars Chicago, IL 60607 $130,000 - $155,000 a year, Pulled from the full job description

• AD&D insurance

• 401(k)

• Health insurance

• Paid time off

• Vision insurance

• Dental insurance

• Flexible spending account, Applicable only to applicants applying to a position in any location with a pay disclosure requirements under state or local law:

• The compensation range that is described below is the possible base pay compensation that the company believes in good faith that it will pay for this role at the time of posting based on job grade for the position. Individual compensation within this range is based on many factors such as years of experience etc. so the company might pay more or less than the posted range and it is understood that this range may be modified in the future.

• In addition to base compensation, MonoSol provides a yearly incentive compensation bonus, a profit sharing bonus when eligible, a comprehensive benefits package including medical, dental, vision insurances, short term disability, long term disability, accidental death and dismemberment, term life insurance, voluntary term life insurance, transit flexible spending account (if applicable), employee assistance program, identity theft protection, 401k and paid time off (vacation and sick days).

Compensation range - $130,000.00 - $155,000.00

Incentive Compensation Bonus Target - 10%

Paid time off amount - 15 days