Description
ArcelorMittal Europe Long Products (AME LP) is operating a network of 10 steel plants, 17 rolling mills and multiple downstream units, producing and commercializing over 9 million tons per year.
Aiming to optimize its operations, AME LP is looking for a Full-Stack Data Scientist to join the central marketing department to take a key role in the pricing team and the digitalization transformation. Pricing is the motor driving any industry. Our team specializes in delivering analytics/AI/ML based pricing solutions to various businesses within the ArcelorMittal Long Products organization. As a part of this role you will get a chance to work with the latest technologies in the domain of ML and data science in general. The main responsibilities will include designing, delivering, and maintaining end-to-end production ready AI and pricing solutions. This involves interaction with marketing and sales teams to continuously improve pricing solutions through better business domain knowledge.
Your role:
As V.I.E Full-Stack Data Scientist, you will play a vital role in helping to turn ideas into data solutions that improve pricing strategies and processes in our commercial function. You will interact with IT, finance, R&D and sales teams to support the performance of the business divisions and drive multi-system developments from design to deployment. You also will energetically support the change management.
Key Responsibilities:
Your will play a key role in commercial strategic and data-driven solutions and will be responsible to :
Design, implement, and maintain robust IT infrastructure to support data science workflows, including Kubernetes-based deployment environments and CI/CD pipelines.
Maintain and enhance internal React-based web application used by the data science team.
Collaborate with data scientists to ensure seamless deployment of models as APIs into production environments using MLOps best practices.
Contribute to the design and implementation of new algorithms for pricing in various business domains.
Continuously improve and refactor existing pricing models for performance, scalability, and maintainability.
Build tools and processes to monitor model performance, data quality, and system reliability.
Ensure adherence to programming best practices, including code quality, documentation, and security standards.