Accountabilities

Key Responsibilities

• Data Collection & Preparation
  • Gather large-scale industrial data from various sources, including IoT sensors, MES, SAP systems, and external data repositories
  • Clean, preprocess, and structure raw data to ensure accuracy and usability
• Exploratory Data Analysis (EDA) & Feature Engineering
  • Conduct exploratory data analysis to identify patterns, correlations, and anomalies in industrial processes
  • Develop and engineer new features to improve model performance and extract meaningful insights
• Model Development & Optimization
  • Select and implement appropriate machine learning algorithms for predictive maintenance, process optimization, and quality improvement
  • Train and fine-tune models, ensuring they generalize well to real-world scenarios
  • Evaluate models using performance metrics and validation techniques
• Deployment & Monitoring
  • Collaborate with IT and software engineering teams to deploy models into production environments
  • Monitor deployed models, detect performance drifts, and retrain models as needed
• Industrial Use Cases & Decision Support
  • Work on data-driven use cases such as quality, predictive maintenance, energy optimization, and supply chain forecasting
  • Support business and operational teams by providing insights and recommendations based on model outputs
• Collaboration & Communication
  • Work closely with cross-functional teams, including domain experts, business analysts, and industrial engineers
  • Translate complex data findings into actionable business insights, presenting them to both technical and non-technical stakeholders
• Coaching and mentoring
  • Coach and mentor junior data scientists (including GCC roles) on advanced data science/modelling techniques
  • Lead multiple data science projects and guide junior team members on each project
• Research & Continuous Improvement
  • Stay up to date with advancements in AI, machine learning, and industrial data science
  • Experiment with emerging technologies and methodologies to enhance data science capabilities within Aperam

Profile

As a Data Scientist, you:

• Hold a Master’s or PhD in Data Science, Computer Science, Mathematics, or a related field
• Have 7+ years of experience in data science, preferably in an industrial or manufacturing environment
• Have led a team of junior data scientists and coached/mentored them in projects
• Are proficient in Python, SQL, and machine learning libraries (Scikit-learn, TensorFlow, PyTorch, etc.)
• Have strong experience in exploratory data analysis (EDA), feature engineering, and statistical modeling
• Have a solid understanding of time-series analysis, predictive maintenance, and industrial AI applications
• Are familiar with big data platforms and cloud environments (Azure, Databricks, Microsoft Data Fabric), and experience with Spark (PySpark or Scala) for big data processing is a plus
• Have experience with data visualization tools such as Power BI, Matplotlib, or Plotly
• Possess excellent problem-solving and analytical skills, with the ability to work on complex datasets
• Have strong communication skills and can explain technical findings to non-technical audiences
• Basic familiarity with the concepts of Computer Vision and Generative AI is a differential
• Are fluent in English (both spoken and written); knowledge of French, or Dutch is a plus
• Should be already working in the EU and ready to relocate to Genk