Data Scientist

McCormick & Company•Hunt Valley, MD

8d•Onsite

About The Position

McCormick & Company, Inc. is seeking a Data Scientist to create advanced analytical models and solutions that provide real-time insights for decision-makers, focusing on ROI and business impact. This role involves enhancing data collection, processing and verifying data for integrity, and developing end-to-end data validation scripts using Python for large-scale data migrations between SQL and NoSQL environments. The Data Scientist will translate data into business insights using Machine Learning, Python, SQL, and Azure ML to optimize manufacturing, supply chain, and product innovation. Responsibilities include designing and deploying LLM-powered web interface bots for manufacturing environments, driving the development of scalable data pipelines and machine learning workflows on cloud platforms like Azure, building and optimizing Spark ETL applications, and building and deploying predictive scoring models. The role also involves statistical modeling, developing and deploying machine learning models on HR datasets (sentiment analysis, retention risk prediction), and automating business processes. Additionally, the Data Scientist will research industry best practices to develop new data analytics capabilities, drive digitalization, and enhance business process automations. Collaboration with cross-functional teams (procurement, R&D, production) to align data-driven strategies with business goals is essential, as is serving as a liaison between technical and non-technical stakeholders.

Requirements

Master’s degree in Data Analytics/Science, Computer Science or related field.
24 months of experience in the job offered or related occupation.
2 years’ experience developing and executing end-to-end data validation scripts using Python for large-scale data migrations between database environments including SQL and NoSQL environments.
2 years’ experience with PowerBI and Tableau data visualization software.
2 years’ experience developing predictive models.
2 years’ experience building and optimizing Spark ETL applications.
2 years’ experience designing and deploying scalable data ingestion and processing pipelines on cloud infrastructure using Azure, AWS or Google.
2 years’ experience using MLflow experiment tracking and model lifecycle management tools for logging, versioning, and deploying machine learning models in cloud environments.
1 year experience with SAP ERP system, specifically, APO and SAP S/4HANA supply chain modules.

Responsibilities

Create advanced analytical models and solutions to provide real-time insights for decision-makers focusing on ROI and business impact using Python, Tableau and PowerBI.
Enhance data collection procedures.
Process and verify data to ensure integrity.
Develop and execute end-to-end data validation scripts using Python for large-scale data migrations between database environments including SQL and NoSQL environments.
Translate data and analytics into business insights using Machine Learning, Python, SQL and Azure ML to optimize manufacturing, supply chain, and product innovation.
Design and deploy LLM-powered web interface bots for manufacturing environments, enabling non-technical users to interact with complex operational data and automated workflows through conversational interfaces.
Drive the development of scalable data pipelines and machine learning workflows using cloud-based platforms including Azure (DevOps, ML, ADLS Gen2).
Build and optimize Spark ETL applications.
Build and deploy predictive scoring models.
Complete statistical modeling.
Develop and deploy machine learning models on HR datasets, including sentiment analysis of employee survey responses and predictive modeling to identify retention risk and drive data-driven workforce strategy and automating business processes.
Research industry best practices and skills to develop new capabilities for data analytics for the Company and drive its digitalization strategy and business process enhancements and automations.
Collaborate with cross-functional teams, including procurement, R&D and production, to align data-driven strategies with business goals.
Serve as a liaison between technical and non-technical stakeholders, ensuring alignment and understanding.