Senior Data Scientist

About ArcelorMittal Global Solutions AI Team

The ArcelorMittal Global Solutions AI Team is a transversal Artificial Intelligence capability enabling business divisions to create measurable value through responsible, production-grade AI and data solutions. We support the full lifecycle of AI initiatives across Finance, HR, Supply Chain, Logistics, Manufacturing, and Operations, while owning technical delivery, quality, and risk controls.

Our portfolio includes document automation, forecasting, optimization, computer vision, predictive maintenance, AI-powered assistants, and GenAI-driven enterprise applications.

Role Summary

As a Senior Data Scientist / Machine Learning Engineer, you will design, build, and operate end-to-end AI and machine learning solutions for the AMGS AI Team.

In the initial phase, you will work hands-on, primarily on Microsoft Azure, performing data analysis, training ML/DL models, and building AI applications, including LLM-powered solutions. As the team grows, you will progressively lead and mentor other data scientists and ML engineers, oversee their work, and ensure high standards of technical quality, robustness, and delivery across AI solutions.

While Azure will be the primary platform, you are expected to expand into AWS over time as AMGS AI solutions evolve.

Key Responsibilities

Hands-on AI / ML Development (Initial Phase)

• Analyze structured and unstructured data to extract insights and define AI use cases
• Design, train, evaluate, and improve ML and DL models
• Build AI solutions across domains such as NLP, computer vision, forecasting, and optimization
• Design and implement GenAI solutions using API-based LLMs (e.g. RAG, agents)
• Decide on the most appropriate approach (LLMs, classical ML, DL, or hybrid) based on the problem
• Develop production-ready AI components and services

AI Engineering & Platform Enablement

• Integrate AI models and LLM-based components into applications and workflows
• Collaborate with software developers and cloud engineers on deployment and integration
• Support monitoring, evaluation, and continuous improvement of AI systems
• Contribute to AI architecture, best practices, and reusable components

Technical Leadership (As Team Grows)

• Guide and mentor other data scientists and ML engineers
• Review code, models, and solution designs
• Define and enforce standards for data science, ML engineering, and GenAI development
• Ensure development quality, robustness, and maintainability of AI solutions

Cross-Functional Collaboration

• Work closely with business analysts, software developers, cloud engineers, and domain experts
• Translate business problems into measurable AI and data solutions
• Support adoption and change management for AI-driven applications

Required Skills & Experience

AI / ML Expertise

• Strong experience in data science and machine learning in production environments
• Deep expertise in at least one ML domain (e.g. NLP, Computer Vision, Forecasting)
• Broad understanding of other ML domains and techniques
• Strong Python programming skills
• Experience training, evaluating, and improving ML/DL models

GenAI & Modern AI Patterns

• Hands-on experience with LLM-based applications
• Up-to-date knowledge of modern GenAI patterns (e.g. RAG, agents, prompt design)
• Ability to assess limitations, risks, and trade-offs of GenAI approaches

Cloud & Engineering

• Experience developing AI solutions on Azure
• Ability and willingness to work with AWS as needed
• Solid software engineering fundamentals (modularity, testing, version control)
• Ability to work independently and own AI solutions end-to-end

Professional Experience

• Proven experience delivering AI/ML solutions in real-world projects
• Experience working in cross-functional teams
• Experience in complex or enterprise environments

Nice to Have

• Experience with MLOps practices and tooling
• Experience deploying and monitoring ML models in production
• Experience building AI-powered APIs or services
• Familiarity with responsible AI, explainability, and model governance

What We Look For in This Role

• A hands-on AI engineer who enjoys solving real business problems
• A strong data scientist with engineering mindset
• A future technical leader who can grow and guide an AI team
• Someone who can balance innovation with robustness and maintainability
• A pragmatic decision-maker who chooses the right AI approach for each problem

What This Role Is Not

• A purely research-focused role
• A PoC-only or experimentation-only position
• A narrow specialization without system-level thinking

If you enjoy building production-grade AI solutions, working across ML and GenAI, and growing into a technical leadership role, this position offers the opportunity to shape how AI is delivered within AMGS AI.