Data Scientist Quality Engineer

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.