Senior Data Scientist

Senior Data Scientist (Project Management)
About Us

Founded in 1908, Merz is a successful, family-owned specialty healthcare company with a rich history. As a leading global aesthetics business, our award-winning portfolio of injectables, devices, and skincare products empowers healthcare professionals to enhance confidence through aesthetic medicine. Our purpose is to fuel confidence by helping people look better, feel better, and live better. We believe you do not have to choose between living life and making a living.

Live your best life with Merz Aesthetics.

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.

• 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, and ensure 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., and maintain dashboards presenting real‑time visualization of ongoing manufacturing operations.
• Data Quality & Governance:
  Implement data quality checks, data contracts, and ongoing monitoring frameworks, and collaborate 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 and partner with data engineering to contribute to CI/CD practices.
• Bachelor's Degree in Data Science
• Bachelor's Degree in Computer Science
• 5+ years Analytics, data science, or advanced analytics engineering experience, including at least 2 years in manufacturing, OT/IIoT, or regulated environments.
• 3–5 years practical experience with UNS/IIoT frameworks (AMQP, OPC UA), contextualization of HMI/PLC/SCADA/MES data, data modeling techniques (Kimball/Dimensional), SQL, Python, and BI platforms (Power BI/Tableau). Experience with data quality, governance, lineage, cataloging, and metric definitions.
• 3–5 years knowledge of GxP/CSV, ALCOA+, and regulated analytics delivery requirements.

• 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).

• 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 skills and capability to apply best practices and integrate business knowledge with the own area
• Strategic…