Lead Data Scientist

The Team :

• As a member of the Commodity Insights you will work on building and deploying ML/LLM powered products and capabilities to power natural language understanding, data extraction, information retrieval and data sourcing solutions for S&P Global Market Intelligence and our clients.
• You will be responsible for the management, deployment and optimization of ML Models and pipelines while leading-by-example in a highly engaging work environment. You will work in a (truly) global team and encouraged for thoughtful risk-taking and self-initiative.

What s in it for you:

• Exciting tasks and the team with own product development in the space of intelligent data solutions based on Machine Learning

Responsibilities:

• Play a central role in all stages of the AI product development life cycle, including:
• Designing Machine Learning systems and model scaling strategies
• Research & Implement ML and Deep learning algorithms for production
• Run necessary ML tests and benchmarks for model validation
• Fine-tune, retrain and scale existing model deployments
• Extend existing ML library s and write packages for reproducing components
• Partner with business leaders, domain experts, and end-users to gain business understanding, data understanding, and collect requirements
• Interpret results and present them to business leaders
• Manage production pipelines for enterprise scale projects
• Perform code reviews & optimization for your projects and team
• Lead and mentor by example, including project scrums

Technical Requirements:

• Proven track record as a ML engineer
• Expert proficiency in Python (Numpy, Pandas, Spacy, Sklearn, Pytorch/TF2, HuggingFace etc.)
• Excellent exposure to large scale model deployment strategies and tools
• Excellent knowledge of ML & Deep Learning domain
• Solid exposure to Information Retrieval, Web scraping and Data Extraction at scale
• Knowledge in AWS Technologies: Terraform, CFT, Lambda, Fargate, ECS and also in SQL, containerization and Django.
• Experience with SOTA models related to NLP like Classification, Summarization, Phrase extraction, Table Extraction and OCR
• Open to learning new technologies and programming languages as required
• Knowledge in model monitoring and retrain & tuning of models

Good to have:

• 4+ years of relevant experience in ML Engineering
• Prior substantial experience regarding document processing