Manufacturing Data Scientist

About the role

The Manufacturing Data Scientist at TAMKO is integral to executing AI and data-driven analytics strategies that perfect manufacturing processes. They work within a cross-functional Business Process Transformation team to design, build, and deploy machine learning models, predictive and prescriptive analytics, and end-to-end, multi-agent systems.

Responsibilities

• Assist in project scoping, execution, and completion that align with TAMKO’s Autopilot End State Goals, delivering MVPs that demonstrate value to stakeholders.
• Develop and deploy machine learning, predictive analytics, and prescriptive analytics, including time-series anomaly detection, predictive maintenance, soft sensors, forecasting, and Digital Twins.
• Extract, contextualize, and engineer features from plant data sources such as process historian, OPC UA, MQTT streams, manufacturing execution systems, maintenance work orders, and quality systems.
• Design and build end-to-end, multi-agent solutions that close the loop from sensing and diagnostics, through root-cause analysis, recommended and executed actions, and verification of outcomes.
• Ground systems in plant knowledge through retrieval-augmented generation over sources such as SOPs, manuals, and work orders, with rigorous evaluation, guardrails, and source citations.
• Deploy models to production, monitor, detect drift, and retrain them to run reliably on the line.
• Apply Six Sigma and statistical process control concepts in code, fusing classical statistics with machine learning to reduce variation, improve process capability, and enhance quality.
• Help advance solutions toward greater automation and autonomous control, applying appropriate validation, monitoring, and safeguards at each stage.
• Perform data visualization and statistical analysis to reduce waste, improve availability and uptime, and enhance quality in manufacturing processes.
• Critically evaluate emerging methods, tools, and vendor claims, distinguishing demonstrated capability from marketing and validating new approaches against proven baselines before deploying them in production.
• Present findings, prototypes, and recommendations to peers, managers, operators, and executives through clear reports, business correspondence, and compelling presentations that translate technical results into business value.

Requirements

• Bachelor’s degree in Mathematics, Science, Engineering, Computer Science, Data Science, Statistics, or a related field.
• 4 to 10 years of related work experience and/or training, or an equivalent combination of education and experience.
• Strong analytical skills and attention to detail.
• Proficiency in Python and SQL for data analysis, modeling, and machine learning.
• Hands-on experience building and validating machine learning models on tabular and time-series data, including feature engineering, cross-validation, and methods such as regression, tree-based models, and anomaly detection.
• Understanding of applied statistics and statistical process control, including control charts, variation, cause and effect relationships, the Pareto principle, process capability, design of experiments, and hypothesis testing, with the ability to perform these analyses in code.
• A production mindset that extends beyond notebooks, including version control and reproducible, maintainable work that can be deployed and monitored.
• Strong problem-solving skills, with the ability to define problems, collect data, establish facts, and draw valid conclusions.
• Ability to read, analyze, and interpret technical procedures, professional and scientific literature, and governmental regulations.
• Ability to draft reports, business correspondence, and procedure manuals.
• Ability to effectively present information and respond to questions from groups of peers, managers, operators, and executives, translating technical results into business value.
• Comfort delivering minimum viable products under tight timelines and operating effectively amid ambiguity and evolving requirements.
• Ability to work independently and as part of a team to drive projects to completion.

Qualifications

• Master’s or PhD in Computer Science, Data Science, Statistics, or a related field.
• Experience with manufacturing and operational technology data systems, such as AVEVA or OSIsoft PI historian, OPC UA, MQTT, and integration with manufacturing execution, maintenance, and quality systems.
• Experience deploying and maintaining models in production (MLOps), including tools such as MLflow, Docker, continuous integration and delivery, and drift monitoring (Evidently or NannyML).
• Experience with generative AI and agentic systems, including retrieval-augmented generation with vector databases, large language model evaluation and guardrails, agent orchestration frameworks, and the Model Context Protocol (MCP).
• Experience with predictive maintenance and reliability methods, such as time-series anomaly detection, survival or time-to-event analysis, and condition monitoring.
• Experience with advanced process control and optimization, such as soft sensors, model predictive control, Bayesian optimization, and design of experiments.
• Experience with deep learning frameworks such as PyTorch or TensorFlow, along with the judgment to favor tree-based methods such as XGBoost or LightGBM for tabular plant data.
• Experience with computer vision for automated quality inspection, including defect and anomaly detection and edge deployment.
• Familiarity with the modern data and lakehouse stack, such as Spark or PySpark, Delta Lake, and dbt, along with tools such as Polars and DuckDB; proficiency in Minitab or JMP is a plus.
• Awareness of emerging methods worth piloting and benchmarking against proven baselines, such as time-series foundation models, knowledge graphs and GraphRAG, physics-informed machine learning, reinforcement learning for control, and causal inference.
• Experience implementing AI and machine learning solutions in the manufacturing industry.
• Strong communication and teamwork skills.

Benefits

This job description is intended to describe the general nature of the work expected. It is not intended to be an exhaustive list of all responsibilities, duties, or skills required. TAMKO offers a comprehensive benefits package, including Group Health and Life Insurance, Vision and Dental Insurance, a Flexible Benefits Plan, a 401(k) Retirement Plan with company match, a Profit Sharing Retirement Plan, and other valuable benefits.