Data Scientist

S&P Global•New York, NY

23h•Onsite

About The Position

The Collection Platforms & AI team is looking for a Data Scientist to build ML-powered products and capabilities for natural language understanding, data extraction, information retrieval, and data sourcing solutions for S&P Global and its clients. The role involves spearheading the development of production-ready AI products and pipelines, working in a global team environment that encourages thoughtful risk-taking and self-initiative. This position offers the opportunity to develop next-generation products and enhance existing ones to solve high-impact business problems, working end-to-end from inception to productionizing solutions at an enterprise scale within a highly skilled technical team.

Requirements

Strong grasp of statistics, probability, and the mathematics underpinning modern AI.
Linear programming and optimization.
Multi-dimensional optimizers, such as Adam, SGD, Gradient Descent …
Ability to adjust weights for full/partial tuning of LLMs.
Hands-on experience with any large language models (e.g., OpenAI, Anthropic, Llama), prompt engineering, fine-tuning/customization, and embedding-based retrieval.
Intermediate proficiency in Python (NumPy, Pandas, SpaCy, scikit-learn, PyTorch/TF 2, Hugging Face Transformers).
Understanding of ML & Deep Learning models, including architectures for NLP (e.g., transformers), GNNs, and multimodal systems.
Solid understanding of database structures and SQL.
Ability to perform independent research and synthesize current AI/ML research, with a track record of applying new methods in production.
Experience in end-to-end GenAI or advanced NLP projects, such as NER, table extraction, OCR integrations, or GNN solutions.
Familiarity with orchestration and deployment tools: Airflow, Redis, Flask/Django/FastAPI, SQL, R-Shiny/Dash/Streamlit.
Openness to evaluate and adopt emerging technologies and programming languages as needed.
Public contributions or demos on GitHub, Kaggle, StackOverflow, technical blogs, or publications.
Indefinite right to work within the USA.

Responsibilities

Develop and deploy large-scale ML and GenAI-powered products and pipelines.
Own all stages of the data science project lifecycle, including: Develop, deploy, monitor, and scale models through the full Software Development Life Cycle into production (including both ML and GenAI services).
Perform exploratory data analysis, proof-of-concepts, model benchmarking, and validation experiments for both ML and GenAI approaches.
Partner with business leaders, domain experts, and end-users to gather requirements and align on success metrics.
Follow coding standards, perform code reviews, and optimize data science workflows.
Evaluation, interpretation, and communication of results to executive stakeholders.