Position Summary
The Operations Data Scientist will be responsible for developing and applying advanced analytics, statistical models, and machine learning techniques to improve decision-making and operational efficiency across Industrial Operations. This role will work closely with business stakeholders in Industrial Operations to transform operational data into actionable insights and will play a foundational role in establishing the Data Science and AI function within Operations, setting up ways of working, best practices, and initial impact‑driven projects.
Core Responsibilities
* Establish the Data Science function within Operations: define methodologies, frameworks, and standards for analytics and AI projects, ensuring scalability and sustainability.
* Partner with Industrial Operations teams to identify and frame business problems that can be addressed with advanced analytics and AI.
* Develop, test, and deploy statistical models, machine learning algorithms, AI, and optimization solutions to drive improvements in efficiency, quality, and reliability.
* Perform exploratory data analysis and hypothesis testing to discover trends, correlations, and root causes in complex datasets.
* Collaborate with IT and Data Governance teams to ensure access to reliable, clean, and compliant data sources.
* Create clear visualizations and concise reports to communicate findings and recommendations to non-technical stakeholders.
* Support the development of digital supply chain initiatives, enhancing traceability, forecasting, and decision support across the product lifecycle.
* Ensure that all models and analyses adhere to compliance and regulatory standards (GxP, data integrity, audit readiness and AI regulations).
* Explore and apply emerging technologies such as Generative AI to improve documentation, SOP training, audit support, and automation of knowledge.
* Monitor and measure the impact of initiatives using KPIs such as ROI, productivity, OEE improvement, or reduction of deviations.
* Contribute to building a data‑driven culture by supporting the training and development of data citizens within Operations.
* Promote the responsible and ethical use of AI, aligning with corporate policies and regulatory requirements.
Required Education and Experience
* Master’s degree in Data Science, Statistics, Computer Science, Engineering, Applied Mathematics, or a related quantitative field.
* Minimum 5 years of professional experience applying machine learning, statistical analysis, and data modeling in a business or industrial environment.
* Proven hands‑on experience in Python, R, or similar programming languages for data science.
* Hands‑on experience with data analysis and machine learning tools (e.g. Pandas, scikit‑learn, TensorFlow, PyTorch).
* Knowledge of database languages (e.g. SQL).
* Experience in deploying data science projects in collaboration with business stakeholders to drive business value.
* Strong background in applied statistics, predictive analytics, and optimization techniques.
* Good coding practices such as usage of code repositories (e.g. git) and writing code documentation.
* Hands‑on experience in using AI/ML tools in an industrial manufacturing and supply chain context.
* Excellent communication skills, able to engage both technical and non‑technical stakeholders.
* Familiarity with using cloud environments to store, annotate and analyze data (e.g. Azure, GCP, AWS).
Preferred Skills & Competencies
* Previous experience in pharmaceutical, life sciences, or manufacturing environments, ideally with exposure to supply chain, production, or quality operations.
* Knowledge of data governance, master data management, and data integrity requirements in regulated industries.
* Familiarity with visualization and BI tools (Power BI, Tableau) for communicating insights.
* Proficiency with cloud data environments (Azure, AWS, GCP), MLOps practices and cloud computing tools (e.g. Databricks).
* Strong business acumen with the ability to translate analytical findings into operational impact and ROI.
* Curiosity, problem‑solving mindset, and a collaborative approach.
* Experience with the development and implementation of AI tools.
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