Lead Data Scientist

S&P Global•New York, NY

21h•$189,532 - $206,350•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 (GAI), Natural Language Processing (NLP), and Large Language Models (LLMs) for batch and streaming AI/ML pipelines. The role architects solutions across data ingestion and preprocessing, search and retrieval, Retrieval Augmented Generation (RAG), fine-tuning, and prompt engineering, using tools and frameworks such as Python, PyTorch, and Hugging Face Transformers. The Lead Data Scientist works with data science, MLOps, technology, and engineering teams to align solutions with business and technical requirements, evaluate and optimize models, and deploy reliable, scalable AI applications to production and cloud platforms. The position also develops and maintains documentation, follows standards and governance frameworks established by senior Data Science and MLOps leadership, and leads troubleshooting of complex model-development and data-pipeline issues. The role is based at 55 Water Street, New York, NY, with telecommuting permitted from anywhere within the U.S. The anticipated base salary range is $189,532 to $206,350, and the position is eligible for an annual incentive plan and S&P Global benefits.

Requirements

Master’s degree in Computer Science, Data Science, or a related IT field.
Three years of experience in the job offered or a related occupation.
Three years of experience with Python programming and machine learning frameworks and statistical libraries, including PyTorch, Hugging Face Transformers, and LangChain.
Three years of experience with LLM architectures, including transformer-based models such as GPT, BERT, or T5.
Three years of experience training, fine-tuning, and adapting large-scale LLMs for domain-specific applications.
Three years of experience developing NLP solutions using traditional and transformer-based architectures.
Three years of experience with text generation, summarization, classification, question answering, and entity extraction as part of implementing scalable information retrieval systems.
Three years of experience with traditional keyword-based search and semantic search using vector embeddings and approximate nearest neighbor techniques.
Three years of experience with open-source and research contributions related to machine learning or Artificial Intelligence.
Three years of 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 covering data ingestion, preprocessing, search and retrieval, RAG, fine-tuning, and prompt engineering.
Use Python, PyTorch, Hugging Face Transformers, and other industry-standard tools and frameworks for model development and fine-tuning.
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 measuring model performance, accuracy, consistency, and reliability during development and UAT.
Monitor and apply model optimizations to improve system efficiency and support 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 defined by senior Data Science and MLOps leadership.
Lead troubleshooting of complex issues in ML model development and data pipelines, devising solutions to improve robustness and performance.