As a member of the EDO, Collection Platforms & AI Cognitive Engineering team you will work on designing and building GenAI-driven and ML-powered products and capabilities to power natural language understanding, data extraction, information retrieval and data sourcing solutions for S&P Global. You will help define AI strategy, mentor junior team members and drive production-ready AI products and pipelines while leading by example in a highly engaging work environment. You will work in a (truly) global team and be encouraged for thoughtful risk-taking and self-initiative.

Whats in it for you :

- Be a part of a global company and build solutions at enterprise scale

- Lead and grow a highly skilled, hands-on technical team (including mentoring junior data scientists)

- Contribute to solving high-complexity, high-impact problems end-to-end

- Architect and oversee production-ready pipelines from ideation to deployment

Responsibilities :

- Define AI roadmap, tooling choices, and best practices for model building, prompt engineering, fine-tuning, and vector retrieval systems

- Architect, develop and deploy large-scale ML and GenAI-powered products and pipelines

- Design and implement MLOps practices including CI/CD pipelines for ML models, experiment tracking, model versioning, and automated testing frameworks.

Own all stages of the data science project lifecycle, including :

- Identification and scoping of high-value data science and AI opportunities

- Partnering with business leaders, domain experts, and end-users to gather requirements and align on success metrics

- Evaluation, interpretation, and communication of results to executive stakeholders

- Lead exploratory data analysis, proof-of-concepts, model benchmarking, and validation experiments for both ML and GenAI approaches

- Establish and enforce coding standards, perform code reviews, and optimize data science workflows

- Drive deployment, monitoring, and scaling strategies for models in production (including both ML and GenAI services)

- Mentor and guide junior data scientists; foster a culture of continuous learning and innovation

- Manage stakeholders across functions to ensure alignment and timely delivery

Technical Requirements :

- Hands-on experience with large language models (e.g., Gemini, OpenAI, Anthropic, Llama), prompt engineering, fine-tuning/customization, and embedding-based retrieval

- Expert proficiency in Python (NumPy, Pandas, SpaCy, scikit-learn, PyTorch/TF 2, Hugging Face Transformers)

- Deep understanding of ML & Deep Learning models, including architectures for NLP (e.g., transformers), GNNs, and multimodal systems

- Strong grasp of statistics, probability, and the mathematics underpinning modern AI

- Ability to surf and synthesize current AI/ML research, with a track record of applying new methods in production

- Proven experience on at least one end-to-end GenAI or advanced NLP project : custom NER, table extraction via LLMs, Q&A systems, summarization pipelines, OCR integrations, or GNN solutions

- Familiarity with MLOps and deployment tools : Redis, Flask/Django/FastAPI, SQL, R-Shiny/Dash/Streamlit, MLflow

- Openness to evaluate and adopt emerging technologies and programming languages as needed

Good to have :

- Masters or Bachelors in Computer Science, Statistics, Mathematics, or related field

- 4 - 8 years of relevant experience in Data Science/AI, with at least 2 years in a leadership or technical lead role

- Prior experience in the Economics/Financial industry, especially with market-intelligence or risk analytics products

- Public contributions or demos on GitHub, Kaggle, StackOverflow, technical blogs, or publications