Senior Specialist

Senior Specialist - AI, Digital Transformation and Data - UI and Mobile Listing reference: arcmt_ Listing status: Under Review Apply by: 21 May 2025 Position summary Industry: Manufacturing Job category: Other: Engineering, Technical, Production and Manufacturing Contract: Permanent Remuneration: Market Related EE position: No About our company Part of the worldwide ArcelorMittal group with an industrial presence in 27 countries, ArcelorMittal South Africa Limited is the largest steel producer on the African continent, with a production capacity of 5 million tonnes of liquid steel per annum. Both flat and long steel is produced in hundreds of grades and specifications. Steel is as relevant as ever to the future success of our world. As one of the only materials to be completely reusable and recyclable, it will play a critical role in building the circular economy of the future. Steel will continue to evolve, becoming smarter, and increasingly sustainable. At ArcelorMittal, our goal is to help build a better world with smarter steels.At ArcelorMittal South Africa, our "We Care" value drives our unwavering commitment to safety and zero harm for our employees. We seek applicants who share this dedication and are ready to uphold our high safety standards.It is essential to regularly check your emails for updates regarding your application status. We utilise "Wamly”, a one-way recorded interview platform, to streamline our hiring process. Should you receive an invitation to complete a recorded interview, please ensure it is submitted by the specified deadline. Timely completion of this step is crucial for your application to be considered for the opportunity. We appreciate your diligence in this process and look forward to your participation. Introduction We are seeking a skilled Data Science Senior Specialist. Key responsibilities include developing and implementing machine learning models for manufacturing operations, collaborating with engineering and operations teams, integrating solutions into workflows, and analysing complex datasets. The candidate should be an effective communicator, collaborative, results-oriented, and adaptable. Job key performance areas Develop and implement predictive and prescriptive machine learning models for manufacturing operations. Collaborate with engineering and operations teams to identify opportunities for data-driven improvements. Integrate machine learning solutions into operational workflows to drive efficiency, quality, and reliability. Design scalable and reusable data science solutions in line with best practices. Analyse complex manufacturing datasets to uncover trends, anomalies, and opportunities. Support the business with clear, data-backed insights and strategic recommendations. Maintain and monitor performance of deployed models to ensure long-term effectiveness. Promote data-driven culture through knowledge sharing and collaboration. Qualifications B Degree (NQF7) Engineering, Data Science, Computer Science, or related field. Machine Learning or Business Intelligence related certifications will be advantageous. A postgraduate qualification would be an advantage. Experience Minimum 8 years’ relevant experience in data science, preferably in a manufacturing or operational environment. Strong hands-on experience with supervised learning techniques such as linear and logistic regression, decision trees, random forests, support vector machines, and gradient boosting methods (e.g., XGBoost, LightGBM). Demonstrated expertise in applying supervised models for classification, regression, and prediction of key operational metrics (e.g., quality, downtime, yield). Practical experience with unsupervised learning techniques such as k-means clustering, DBSCAN, hierarchical clustering, and dimensionality reduction methods like PCA and t-SNE. Applied unsupervised methods for root cause analysis, anomaly detection, sensor signal pattern recognition, and process behavior segmentation. Proficient in Python and its machine learning ecosystem, including pandas, NumPy, scikit-learn, matplotlib/seaborn, and preferably TensorFlow or PyTorch. Hands-on experience using Azure Databricks for developing, training, and scaling machine learning models, including data pipeline creation, notebook development, and distributed processing. Exposure to MLOps practices, including model versioning, monitoring, CI/CD pipelines, and automation of retraining using tools like MLflow, Azure Machine Learning, and Git-based workflows. Demonstrated capability in handling large datasets and data from diverse sources (e.g., PLCs, MES, ERP). Experience in deploying, monitoring, and maintaining models in production environments. Experience with Business Intelligence tools (e.g., Power BI) is beneficial. Personal attributes Strong analytical thinking and problem-solving capabilities. Ability to apply data science to real-world manufacturing challenges. Effective communicator with the ability to explain technical concepts to non-technical stakeholders. Collaborative and capable of working in cross-functional teams. Results-oriented with a strong sense of accountability and initiative. High attention to detail and strong organizational skills. Adaptable, self-driven, and committed to continuous learning. Ability to work independently and manage multiple priorities. #J-18808-Ljbffr