Senior Data Scientist

Merz North America•Racine, WI

22h

About The Position

This role serves as the organization's first dedicated data scientist for manufacturing analytics and the Unified Namespace (UNS). The role is responsible for establishing a scalable analytics foundation, harmonizing data across operational and enterprise systems, developing robust data models, delivering high value KPIs and dashboards, and continuously enhancing data quality, speed, and compliance within a regulated environment.

Requirements

Bachelor's Degree Data Science
Bachelor's Degree Computer Science
5+ years Analytics, data science, or advanced analytics engineering, including at least 2 years in manufacturing, OT/IIoT, or regulated environments.
3-5 years Practical expertise with: UNS/IIoT frameworks (AMQP, OPC UA), including contextualization of HMI/PLC/SCADA/MES data.
Data modeling techniques (Kimball/Dimensional), SQL, Python, and BI platforms (Power BI/Tableau).
Data quality, governance, lineage, cataloging, and metric definitions.
3-5 years Knowledge of GxP/CSV, ALCOA+, and regulated analytics delivery requirements.

Nice To Haves

Working with PLC, MES, QMS, and ERP data sources.
Working with Ignition, Litmus, and Fuuz.
Developing, fine tuning, or prompt engineering large language models (e.g., Claude, Copilot) for domain specific tasks.
Hands on experience with model evaluation frameworks (e.g., prompt testing, hallucination detection, benchmark scoring, adversarial testing).
Technical & Functional Skills SQL and Python for analytics, data preparation, and automation Dimensional modeling (Kimball), star/snowflake schemas Semantic model development for BI and self service analytics Contextualization of HMI/PLC/SCADA/MES data Time series analysis and historian integration Statistical analysis, SPC, MSA Predictive modeling and forecasting Data contracts, lineage, cataloging, and metadata management Automated data quality rules and monitoring In-depth knowledge of the above mentioned skills & capability to apply best practices and integrate business knowledge with the own area Strategic thinking & ability to solve complex problems by applying new perspective Ability to explain difficult and/or sensitive information in a clear and structured way & adapt communication style to different audiences Good MS Office package skills & familiarity with the most common AV technology

Responsibilities

UNS Management Own and advance the UNS to deliver a centralized, structured, real-time and historical view of operational data,define and maintain canonical data models, naming conventions, and data standards across OT and enterprise systems, andensure consistency in data lineage, metadata, and contextualization across HMI/PLC, SCADA, MES, QMS, ERP, and complaints systems.
Data Modeling and Analytics Develop and maintain semantic and analytical data models, build production-quality KPI dashboards for common operational concepts such as OEE, yield, scrap, cycle time, etc.,anddevelop and maintain dashboards presenting real-time visualization of on-going manufacturing operations.
Data Quality & Governance Implement data quality checks, data contracts, and ongoing monitoring frameworks, andcollaborate with Quality and Regulatory teams to ensure adherence to GxP data integrity principles (ALCOA+), audit trails, CSV/GAMP 5, 21 CFR Part 11/820, and ISO 13485 requirements.
Cross-Functional Analytics Enablement Translate business, operations, and quality challenges or requirements into actionable analytics solutions,develop self-service semantic layers to empower business users with trusted, curated data, and identify and recommend new data sources, sensors, and integration patterns to close information gaps.
Tooling, Standards, & Acceleration Build and maintain a practical analytics toolkit, including versioned notebooks, templates, and a governed metrics layer andpartner with data engineering to contribute to CI/CD practices.