Data Scientist

Purpose and Mission

Bekaert 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. 

To 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. 

Key Activities and Responsibilities

The data scientist is a role in the Data and Analytics Platform team and reports to Head of Artificial Intelligence. You will play a pivotal role in planning, executing, and delivering machine learning-based projects. Most of the work will be in machine learning (ML) modelling, generative & agentic  AI, management and problem analysis, data exploration and preparation, data collection and integration, operationalization. Data Scientist will be a key interface between Digital Organization, IT Organization, Business Units, Analytics team, and various other IT departments such as security, governance.

Main Responsibilities of Data Scientist are:

●    Problem Analysis and Project Management
              - Guide and inspire the organization about the business potential and strategy of artificial intelligence
              - Identify data-driven / ML business opportunities
              - Collaborate across business teams to understand IT and business constraints
              - Prioritize, scope, and manage data science projects and corresponding key performance indicators (KPIs) for
                success
              - Define and communicate governance principles

●    Data Collection and Integration
              - Understand new data sources and process pipelines and catalog/document them
              - Acquire access to various databases and other sources systems such as SQL or graph databases
              - Create data pipelines for more efficient and repeatable data science projects

●    Data Exploration and Preparation
              - Apply statistical analysis and visualization techniques to various data, such as hierarchical clustering,
                principal components analysis (PCA)Machine Learning
              - Generate hypotheses about the underlying mechanics of the business process
              - Test hypotheses using various quantitative methods
              - Display drive and curiosity to understand the business process to its core
              - Network with domain experts to better understand the business mechanics that generated the data

●    Machine Learning
              - Apply ML and advanced analytics techniques to perform classification or prediction tasks
              - Integrate domain knowledge into the ML solution, for example, from an understanding of financial risk,
                customer journey, quality prediction
              - Validation & optimization of models
●    Generative & Agentic AI
              - Use generative AI to extract insights out of unstructured datasets
              - Use agentic concepts to improve quality of the results

●    Operationalization
              - Collaborate with ML Ops Engineer to evaluate and implement ML deployment options
              - Integrate model performance management tools into the current business infrastructure
              - Implement champion/challenger test (A/B tests) on production systems
              - Continuously monitor execution and health of production ML models
              - Establish best practices around ML production infrastructure

●    Other
              - Train other business and IT staff on basic data science principles and techniques
              - Promote collaboration with other data science teams within and external to organization 
              - Work within an agile delivery methodology in a leading role.

Skills

Competencies
•    Organizationally savvy, with the ability to navigate organizational politics. 
•    Ability to articulate new ideas and concepts to technical and non-technical audiences
•    Ability to understand the long-term ("big picture") and short-term perspectives of situations
•    Ability to translate future-state business capabilities and requirements into solution requirements
•    Ability to propose and estimate the financial impact of solution alternatives
•    Ability to assess product quality and other non-functional attributes and provide recommendations

Characteristics
•    You are a leader and an entrepreneur that people willingly follow
•    Displays intellectual curiosity and integrity
•    Motivated and driven by achieving long-term business outcomes
•    High level motivational skills so that you can also manage virtual teams
•    Good written and verbal communication skills
•    Ability to work within an IT organization in an in-and-out outsourced environment

Previous Experience

•    You will ideally have a specialization in ML, AI, cognitive science or data science.
•    At 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
•    Experience with statistical software, scripting languages, and packages. Python/Jupyter is required. Others (e.g. MATLAB) are appreciated.
•    Experience with the data science platforms - Azure Machine Learning. Others (e.g. Google Cloud ML) are appreciated.
•    Knowledge 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.
•    Experience on Open-source data science libraries (e.g. sklearn, Tensorflow,...), open-source visualisation libraries (e.g.Plotly Dash/Bokeh, Streamlit, ..)
•    Experience with popular database programming languages including SQL
•    Experience of Data Visualisation Products such as Power BI  is a plus.
•    Experience with large language models and prompt engineering
•    Good understanding of agile principles and development methodologies and capability of supporting agile teams by providing advice and guidance on opportunities, impact and risks
•    Good analytical, planning and organizational skills. Be a voice of reason to make tough calls.

Qualifications and Education

•    Master degree or phd in computer science, data science, operations research, statistics, applied mathematics is required.