Lead Data Scientist

S&P Global•New York, NY

23h•Remote

About The Position

Standard & Poor’s Financial Services LLC seeks a Lead Data Scientist to lead the design and development of custom machine learning, Generative AI, Natural Language Processing, and Large Language Model solutions for batch and streaming AI/ML pipelines. The role architects solutions spanning data ingestion and preprocessing, search and retrieval, Retrieval Augmented Generation, fine-tuning, and prompt engineering, using tools and frameworks such as Python, PyTorch, and Hugging Face Transformers. The Lead Data Scientist partners with data science, MLOps, technology, and engineering teams to align solutions with technical and business requirements, develop robust model evaluation frameworks, optimize models, and deploy reliable, scalable, and performant production systems. The role also deploys and manages AI applications on cloud platforms, maintains reproducible documentation, follows standards and governance defined by senior Data Science and MLOps leadership, and leads troubleshooting of complex model-development and data-pipeline issues. The position is based at 55 Water Street, New York, NY, with telecommuting permitted from anywhere within the United States.

Requirements

Master’s degree in Computer Science, Data Science, or a related IT field.
At least 3 years of experience in the job offered or a related occupation.
At least 3 years of experience with Python programming and machine learning frameworks and statistical libraries, including PyTorch, Hugging Face Transformers, and LangChain.
Experience with LLM architectures, including transformer-based models such as GPT, BERT, or T5.
Experience training, fine-tuning, and adapting large-scale LLMs for domain-specific applications.
Experience developing NLP solutions using traditional and transformer-based architectures.
Experience with text generation, summarization, classification, question answering, and entity extraction in scalable information retrieval systems.
Experience with keyword-based and semantic search approaches using vector embeddings and approximate nearest neighbor techniques.
Open-source and research contributions related to machine learning or Artificial Intelligence.
Experience scoping, formulating, and executing complex machine learning projects, including stakeholder management.

Responsibilities

Lead the design and development of custom machine learning, Generative AI, NLP, and LLM solutions for batch and streaming AI/ML pipelines.
Architect solutions for data ingestion, preprocessing, search and retrieval, RAG, fine-tuning, and prompt engineering.
Work with data science, MLOps, and technology teams to align solutions with technical and business requirements.
Collaborate with data science teams to design and implement evaluation frameworks that measure model performance, accuracy, consistency, and reliability during development and UAT.
Monitor and apply model optimizations to improve system efficiency and ensure continuous improvement.
Partner with engineering teams to deploy models into production, ensuring reliability, scalability, and performance.
Deploy and manage AI applications on cloud platforms such as AWS, Google Cloud, or Microsoft Azure.
Develop and maintain documentation of ML modeling processes and procedures to support reproducibility and knowledge sharing.
Ensure models adhere to standards, governance frameworks, and best practices set by senior Data Science and MLOps leadership.
Lead troubleshooting of complex issues in ML model development and data pipelines, developing solutions to improve robustness and performance.