Manufacturing Data Scientist

As a Manufacturing Data Scientist at TAMKO, you will be integral to executing the AI and data-driven analytics strategies that perfect the quality, safety, and productivity of our manufacturing processes. As part of a cross-functional Business Process Transformation team, you will design, build, and deploy machine learning models, predictive and prescriptive analytics, and end-to-end, multi-agent systems in support of TAMKO's Autopilot initiative, which seeks to digitize production lines, ensure equipment reliability, and advance toward autonomous control of manufacturing processes. The focus is on delivering value now, which means contending with real-world data, context, and model reasoning challenges as you build and deploy reliable solutions on the plant floor. This is a hands-on role that pairs disciplined statistics and process knowledge with modern AI engineering, often delivering minimum viable products under tight timelines and evolving

, all while building on TAMKO's deep Deming and Six Sigma foundation layers. Summary of Essential Job Functions To perform this job successfully, an individual must be able to perform each essential function satisfactorily. Reasonable accommodations may be made to enable qualified individuals with disabilities to perform the essential functions. Other duties may also be assigned.

: • Assist in the scoping, execution, and completion of projects that align with TAMKO's Autopilot End State Goals, including the rapid delivery of minimum viable products 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 the process historian and its asset hierarchy, OPC UA and MQTT streams, manufacturing execution systems, maintenance work orders, and quality systems, improving the data and context available to downstream models and AI 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 (for example, Plan-Do-Check-Act), continuously learning and adapting across repeated cycles. • Ground these systems in plant knowledge through retrieval-augmented generation over sources such as standard operating procedures, manuals, and work orders, with rigorous evaluation, guardrails, and source citations. • Deploy models to production. Support monitoring, detecting drift, and retraining them so they run reliably on the line rather than remaining prototypes. • 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 along the path 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 such as cost, scrap, uptime, and risk. • Interpret an extensive variety of technical instructions in mathematical or diagram form and reason across several abstract and concrete variables.