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.
Partnering 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.

5+ years in professional work within AI space or building statistical/mathematical quantitative models in production.
Advanced technical degree (Master and above) in any of Sciences, Technology, Engineering and Mathematics.
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 to expert 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.
Public contributions or demos on GitHub, Kaggle, StackOverflow, technical blogs, or publications.
Openness to evaluate and adopt emerging technologies and programming languages as needed.

Health & Wellness: Health care coverage designed for the mind and body.
Flexible Downtime: Generous time off helps keep you energized for your time on.
Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.
Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs.
Family Friendly Perks: It’s not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families.
Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.

Senior Data Scientist

Key skills