PpAs the world and the way people live is changing, we at Bekaert believe it’s our responsibility to contribute to finding new solutions for the future. With a 140+ year old heritage of excellence, innovation, and a future‑focused mindset, we strive to create value for our customers and society. We aim to do this through innovative solutions and sustainable practices. We are committed to pushing the boundaries of steel wire transformation and coatings whilst also leveraging our expertise to develop innovative solutions with new materials and services in a safe, smart, and sustainable way. Our focus extends to markets such as new mobility, low‑carbon construction, and green energy. /p pAs a dynamic and growing company with over 19,000 employees worldwide, 75 nationalities, a retention rate above 90% and €3.7 billion in combined revenue in 2025, we are looking for someone like you to join our team as we continue to shape a safer, more efficient, and connected world! /p h3bPurpose and Mission /b /h3 pBekaert is leading transformation program to enhance end‑to‑end user experience and deliver user journeys that drive impact across multiple cross‑functional processes. Bekaert’s employees, suppliers and customers across the value chain are getting increasingly sophisticated and expect a seamless and well‑designed user experience. Bekaert organization is reviewing the success and performance of these end‑to‑end user journey through data analytics, reporting and governance across the journey. /p pTo strengthen our data science and analytics capabilities, you will join us in the role of “Data Scientist” in Data and Analytics Platform team. You are accountable to model complex business problems and discover business insights through the use of statistical, algorithmic, mining, and visualization techniques. You will collaborate with cross‑functional stakeholders to understand the business usage of data, architect specialized database and computing environments and communicate recommendations to enable decision‑making. /p h3bKey Activities and Responsibilities /b /h3 ul libProblem Analysis and Project Management /b ul liGuide and inspire the organization about the business potential and strategy of artificial intelligence /li liIdentify data‑driven / ML business opportunities /li liCollaborate across business teams to understand IT and business constraints /li liPrioritize, scope, and manage data science projects and corresponding key performance indicators (KPIs) for success /li liDefine and communicate governance principles /li /ul /li libData Collection and Integration /b ul liUnderstand new data sources and process pipelines and catalog/document them /li liAcquire access to various databases and other source systems such as SQL or graph databases /li liCreate data pipelines for more efficient and repeatable data science projects /li /ul /li libData Exploration and Preparation /b ul liApply statistical analysis and visualization techniques to various data, such as hierarchical clustering, principal components analysis (PCA) /li liGenerate hypotheses about the underlying mechanics of the business process /li liTest hypotheses using various quantitative methods /li liDisplay drive and curiosity to understand the business process to its core /li liNetwork with domain experts to better understand the business mechanics that generated the data /li /ul /li libMachine Learning /b ul liApply ML and advanced analytics techniques to perform classification or prediction tasks /li liIntegrate domain knowledge into the ML solution, for example, from an understanding of financial risk, customer journey, quality prediction /li liValidation optimization of models /li /ul /li libGenerative Agentic AI /b ul liUse generative AI to extract insights out of unstructured datasets /li liUse agentic concepts to improve quality of the results /li /ul /li libOperationalization /b ul liCollaborate with ML Ops Engineer to evaluate and implement ML deployment options /li liIntegrate model performance management tools into the current business infrastructure /li liImplement champion/challenger test (A/B tests) on production systems /li liContinuously monitor execution and health of production ML models /li liEstablish best practices around ML production infrastructure /li /ul /li libOther /b ul liTrain other business and IT staff on basic data science principles and techniques /li liPromote collaboration with other data science teams within and external to organization /li liWork within an agile delivery methodology in a leading role /li /ul /li /ul h3bSkills /b /h3 ul libCompetencies /b ul liOrganizationally savvy, with the ability to navigate organizational politics /li liAbility to articulate new ideas and concepts to technical and non‑technical audiences /li liAbility to understand the long‑term ("big picture") and short‑term perspectives of situations /li liAbility to translate future‑state business capabilities and requirements into solution requirements /li liAbility to propose and estimate the financial impact of solution alternatives /li liAbility to assess product quality and other non‑functional attributes and provide recommendations /li /ul /li libCharacteristics /b ul liYou are a leader and an entrepreneur that people willingly follow /li liDisplays intellectual curiosity and integrity /li liMotivated and driven by achieving long‑term business outcomes /li liHigh level motivational skills so that you can also manage virtual teams /li liGood written and verbal communication skills /li liAbility to work within an IT organization in an in‑and‑out outsourced environment /li /ul /li /ul h3bPrevious Experience /b /h3 ul liYou will ideally have a specialization in ML, AI, cognitive science or data science /li liAt least 3 years of progressive relevant experience in data science; successfully launching, planning, and executing significant data science projects. Experience building and deploying predictive models, web scrapping, and scalable data pipelines /li liExperience with statistical software, scripting languages, and packages. Python/Jupyter is required. Others (e.g. MATLAB) are appreciated /li liExperience with the data science platforms - Azure Machine Learning. Others (e.g. Google Cloud ML) are appreciated /li liKnowledge and experience in statistical and data mining techniques - generalized linear model /regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, neural network, graph analysis, etc /li liExperience on Open‑source data science libraries (e.g. sklearn, Tensorflow,...), open‑source visualisation libraries (e.g.Plotly Dash/Bokeh, Streamlit, ..) /li liExperience with popular database programming languages including SQL /li liExperience of Data Visualisation Products such as Power BI is a plus /li liExperience with large language models and prompt engineering /li liGood understanding of agile principles and development methodologies and capability of supporting agile teams by providing advice and guidance on opportunities, impact and risks /li liGood analytical, planning and organizational skills. Be a voice of reason to make tough calls /li /ul h3bQualifications and Education /b /h3 ul liMaster degree or PhD in computer science, data science, operations research, statistics, applied mathematics is required /li /ul h3bBe bold and take the leap! /b /h3 pWe're looking for individuals who are not afraid to take risks and explore new ideas. If you are passionate about personal growth and bringing your authentic self to work, we want you on our team! /p pAt Bekaert, we celebrate diversity and are committed to creating an inclusive work environment. We do not discriminate based on race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. /p pTo learn more about us and our exciting career opportunities, visit Bekaert Careers /p /p #J-18808-Ljbffr