MRO and Reliability Data Scientist

As a Data Scientist at Barry Callebaut, your role revolves around building and deploying tools to extract actionable insights from maintenance and production data using machine learning and LLM-based AI. Your main responsibilities will include:

- Developing and deploying data pipelines and AI-powered analytics to cleanse, classify, and interpret maintenance and spare-parts data. - Integrating OpenAI API and other ML models into workflows to scale insights. - Designing, documenting, and maintaining datasets and KPIs used globally in Power BI. - Identifying trends, patterns, and anomalies in asset and work-order data to provide actionable reliability insights. - Owning end-to-end delivery from data extraction to Power BI deployment and adoption by business stakeholders. - Collaborating cross-functionally with Manufacturing Operations, Process Excellence, Engineering, and Sourcing teams to support cost-reduction and reliability programs. - Acting as the global data owner for spare-parts and CMMS master data, driving data cleansing, duplicate elimination, and governance improvements. - Continuously industrializing and documenting solutions to ensure that dashboards, code, and models remain sustainable and reusable. - Promoting the democratization of data through self-service analytics, coaching colleagues to use Power BI datasets and standardized definitions.

In terms of qualifications, you should possess: - Master's or bachelor's degree in engineering, IT, or Data & Computer Science. - 8+ years of experience with Python in machine learning and API calls. - Proficiency in data visualization/reporting tools such as PowerBI, SSRS, Tableau, or SAC. - Basic understanding of manufacturing automation (PLC, SCADA, MES, Historian, OSI PI).

Your essential experience and knowledge should include: - >8 years' experience with Python as a data scientist, with proficiency in data exploration, machine learning, and visualization. - Preferably >1 year exposure to industrial data from processing or manufacturing industry or power plants. - Experience integrating or automating workflows using OpenAI API or similar LLM platforms. - Demonstrated experience in data analysis, KPI development, and reporting. - Robust understanding of maintenance operations and asset management principles. - Reliability and/or maintenance management background is highly desirable, as well as experience with CMMS.

Barry Callebaut is committed to Diversity & Inclusion, fostering an inclusive environment where individuals can grow to their full potential and feel they belong. As a Data Scientist at Barry Callebaut, your role revolves around building and deploying tools to extract actionable insights from maintenance and production data using machine learning and LLM-based AI. Your main responsibilities will include:

- Developing and deploying data pipelines and AI-powered analytics to cleanse, classify, and interpret maintenance and spare-parts data. - Integrating OpenAI API and other ML models into workflows to scale insights. - Designing, documenting, and maintaining datasets and KPIs used globally in Power BI. - Identifying trends, patterns, and anomalies in asset and work-order data to provide actionable reliability insights. - Owning end-to-end delivery from data extraction to Power BI deployment and adoption by business stakeholders. - Collaborating cross-functionally with Manufacturing Operations, Process Excellence, Engineering, and Sourcing teams to support cost-reduction and reliability programs. - Acting as the global data owner for spare-parts and CMMS master data, driving data cleansing, duplicate elimination, and governance improvements. - Continuously industrializing and documenting solutions to ensure that dashboards, code, and models remain sustainable and reusable. - Promoting the democratization of data through self-service analytics, coaching colleagues to use Power BI datasets and standardized definitions.

In terms of qualifications, you should possess: - Master's or bachelor's degree in engineering, IT, or Data & Computer Science. - 8+ years of experience with Python in machine learning and API calls. - Proficiency in data visualization/reporting tools such as PowerBI, SSRS, Tableau, or SAC. - Basic understanding of manufacturing automation (PLC, SCADA, MES, Historian, OSI PI).

Your essential experience and knowledge should include: - >8 years' experience with Python as a data scientist, with proficiency in data exploration, machine learning, and visualization. - Preferably >1 year exposure to industrial data from processing or manufacturing industry or power plants. - Experience integrating or automating workflows using OpenAI API or similar LLM platforms. - Demonstrated experience in data analysis, KPI development, and reporting. - Robust understanding of maintenance operations and asset management principles. - Reliability and/or maintenance management background is highly desirable, as well as experience with CMMS.

Barry