Join MonoSol’s forward thinking Quality team and turn raw manufacturing data into actionable insights that raise product quality and process efficiency. You’ll sit at the intersection of data science and manufacturing engineering to mine and utilize high-frequency production data for building predictive models and guiding teams on the levers that control critical quality attributes. You’ll be an internal expert who bridges advanced data science with practical plant operations and a critical member to drive digital transformation across the company.

Responsibilities

• Leadership and Direction
  • Champion best practices in data governance, reproducibility, and experiment design; contribute to MonoSol’s growing analytics community.
• Data Engineering
  • Connect to historians/MES (manufacturing execution software), write efficient data pipelines to profile large time-series and batch datasets to discover key factors driving manufacturing defects and variability.
• Predictive Modeling & Analytics
  • Train, test, and maintain regression/classification models (e.g. linear regression, XGBoost, TensorFlow). Emphasize and effectively communicate model outputs to key stakeholders.
• Performance Improvement through ML
  • Integrate predictive models with real-time dashboards or control-room alerts that have a positive financial impact 
• Efficient Reporting with Visualization & Storytelling
  • Build clear dashboards (e.g. Power BI, Spotfire, or Custom) and present findings to production, maintenance, and leadership teams
• Model Deployment & Monitoring
  • Package models for deployment in production environments using either cloud-based or on-premises infrastructure as needed. Set up dashboards and alerts to provide near-real-time insights and leading indicators for operators and engineers.
• Data and Analytics Strategy
  • Make recommendations to improve data and analytics systems and platforms, contributing to the continuous improvement and refinement of data and analytics strategy at MonoSol.
• Data Architecture
  • Help define data standards (naming, sampling, governance) for projects.
• Continuous Improvement & Collaboration
  • Partner across manufacturing teams to translate model outputs into actions; coach colleagues on data-driven methods. Translate model findings into root-cause actions.
• Personal Development
  • Stay current on manufacturing analytics, MLOps, and Six Sigma best practices; pursue certifications or conferences as needed.

Typical Tasks

• Extract, clean, and feature-engineer high-velocity plant data from manufacturing systems.
• Design and extract meaningful features from time-series, batch, and categorical data to improve model performance and interpretability.
• Build and evaluate predictive models (regularized linear regression, gaussian process regression, gradient boosting, neural networks); create simulation notebooks for process scenarios.
• Deploy models in production environments (cloud or on-premises) using APIs or integrated systems; monitor performance for drift and retrain as needed to maintain accuracy.
• Perform root cause analysis using data-driven techniques to identify sources of defects or process inefficiencies
• Create intuitive dashboards that link inputs to predicted quality metrics.
• Work closely with quality engineers, process engineers, and production teams to define the problem, validate model results, and co-develop solutions that are both technically sound and operationally practical.
• Document methodologies, models, and findings in a clear and reproducible manner.
• Present insights to operators, engineers, and executives

Qualifications

• Education
  • Bachelor's degree in chemical engineering, Mechanical Engineering, Data Science, Statistics, Computer Science, or related field (required).
  • Master’s or graduate certificate in Data Science, Analytics, or related field (preferred).
• Experience
  • 3+ years of statistical modeling, applied machine learning, data science, or advanced analytics preferably in process control and manufacturing.
  • Proven success in improving yield, uptime, or quality with statistical or ML models.
  • Familiarity with statistical process control (SPC), control charts, and quality metrics.
  • Six Sigma green belt or higher (preferred)
• Technical Skills
  • Proficiency in Python or R for data analysis and modeling. (e.g., pandas/scikit-learn or dplyr/caret/tidymodels).
  • Experience with deep learning frameworks (e.g. TensorFlow, pyTorch)
  • Strong SQL skills and working with relational databases; experience with cloud platforms (AWS SageMaker, Azure ML) is a plus.
• Soft Skills
  • Ability to clearly communicate and translate model outputs into actionable recommendations for manufacturing-floor teams, engineering staff, and executive leadership.
  • Comfortable working in cross-functional settings and driving change.

Additional information

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 - $86,907.87 - $146,142.27

Incentive Compensation Bonus Target – 10%

Paid time off amount – 15 days

Closing

The above statements are intended to describe the general nature and level of the work being performed by employees assigned to this position. This is not intended as an exhaustive list of all responsibilities, duties, and skills required. MonoSol, LLC reserves the right to make changes to the job description whenever necessary.

Disclaimer

As part of MonoSol, LLC’s employment process, finalist candidates will be required to complete a drug / alcohol test, physical, and background check prior to employment commencing. MonoSol, LLC is an equal opportunity employer. All qualified applicants will be considered without regard to race, national origin, gender, age, disability, sexual orientation, veteran status, or marital status.