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POSITION SUMMARY / MISSION

The 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.

The Data Scientist will also play a foundational role in establishing the Data Science and AI function within Operations, starting from the ground up. This includes setting up ways of working, best practices, and initial projects that demonstrate impact. The position will be a key driver of the transformation towards a data-driven and AI-enabled Operations organization, embedding analytics into core processes and fostering a culture of innovation.

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 data base 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 industrial manufacturing and supply chain context

· Excellent communication skills, able to engage both technical and non-technical stakeholders.

· Familiary of using cloud environments to store, annotate and analyse 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.