Data Scientist Manufacturing Quality - Duneland

Monosol-posted 2 months ago

$86,907 - $146,142/Yr

Full-time • Mid Level

Portage, IN

Plastics and Rubber Products Manufacturing

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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.

Champion best practices in data governance, reproducibility, and experiment design; contribute to MonoSol's growing analytics community.
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.
Train, test, and maintain regression/classification models (e.g. linear regression, XGBoost, TensorFlow). Emphasize and effectively communicate model outputs to key stakeholders.
Integrate predictive models with real-time dashboards or control-room alerts that have a positive financial impact.
Build clear dashboards (e.g. Power BI, Spotfire, or Custom) and present findings to production, maintenance, and leadership teams.
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.
Make recommendations to improve data and analytics systems and platforms, contributing to the continuous improvement and refinement of data and analytics strategy at MonoSol.
Help define data standards (naming, sampling, governance) for projects.
Partner across manufacturing teams to translate model outputs into actions; coach colleagues on data-driven methods. Translate model findings into root-cause actions.
Stay current on manufacturing analytics, MLOps, and Six Sigma best practices; pursue certifications or conferences as needed.

Bachelor's degree in chemical engineering, Mechanical Engineering, Data Science, Statistics, Computer Science, or related field (required).
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.
Proficiency in Python or R for data analysis and modeling (e.g., pandas/scikit-learn or dplyr/caret/tidymodels).
Strong SQL skills and working with relational databases; experience with cloud platforms (AWS SageMaker, Azure ML) is a plus.

Master's or graduate certificate in Data Science, Analytics, or related field (preferred).
Six Sigma green belt or higher (preferred).
Experience with deep learning frameworks (e.g. TensorFlow, pyTorch).

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).
Incentive Compensation Bonus Target - 10%.

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