Full-Time

Generative AI Data Scientist

Confirmed live in the last 24 hours

S&P Global

S&P Global

10,001+ employees

Provides financial information and analytics services

Data & Analytics
Financial Services

Compensation Overview

$100.2k - $185kAnnually

Senior

Remote in USA

Category
Natural Language Processing (NLP)
Data Science
AI & Machine Learning
Data & Analytics
Required Skills
Python
Tensorflow
Keras
Pytorch
Natural Language Processing (NLP)
Computer Vision
Requirements
  • Bachelor's / Master’s in Computer Science, Mathematics or Statistics, Computational linguistics, Engineering, or a related field.
  • 5+ years of professional hands-on experience leveraging large sets of structured and unstructured data to develop data-driven tactical and strategic analytics and insights using ML, NLP, computer vision solutions.
  • Demonstrated 4+ years hands-on experience with Python, Hugging Face, TensorFlow, Keras, PyTorch, or similar statistical tools. Expert in python programming.
  • 2+ years hands-on experience developing natural language processing (NLP) models, ideally with transformer architectures.
  • 2+ years of experience with implementing information search and retrieval at scale, using a range of solutions ranging from keyword search to semantic search using embeddings.
  • Knowledge of developing or tuning Large Language Models (LLM) and Generative AI (GAI)
  • Knowledge of NLP, LLMs (extractive and generative), fine-tuning and LLM model development. Familiar with higher level trends in LLMs and open-source platforms.
  • Nice to have: Experience with contributing to Github and open source initiatives or in research projects and/or participation in Kaggle competitions.
Responsibilities
  • ML, Gen AI, NLP, LLM Model Development: Design and develop custom ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines. Model components will include data ingestion, preprocessing, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM model development, fine-tuning and prompt engineering and ensure the solution meets all technical and business requirements. Work closely with other members of data science, MlOps, technology teams in the design, development, and implementation of the ML model solutions.
  • ML, NLP, LLM Model Evaluation: Work closely with the other data science team members to develop, validate, and maintain robust evaluation solutions and tools to evaluate model performance, accuracy, consistency, reliability, during development, UAT. Implement model optimizations to improve system efficiency.
  • NLP, LLM, Gen AI Model Deployment: Work closely with the MLOps team for the deployment of machine learning models into production environments, ensuring reliability and scalability.
  • Internal Collaboration: Collaborate closely with product teams, business stakeholders, Mlops, machine learning engineers, and software engineers to ensure smooth integration of machine learning models into production systems.
  • Documentation: Write and Maintain comprehensive documentation of ML modeling processes and procedures for reference and knowledge sharing.
  • Develop Models Based on Standards and Best Practices: Ensure that the models are designed and developed while adhering to specified standards, governance and best practices in ML model development as specified by senior Data Science and MLOps leads.
  • Assist in Problem Solving: Troubleshoot complex issues related to machine learning model development and data pipelines and develop innovative solutions.

S&P Global provides financial information and analytics to a wide range of clients, including investors, corporations, and governments. The company offers services such as credit ratings, market intelligence, and indices, which help clients understand and navigate the global financial market. S&P Global's products work by utilizing advanced data analytics and research to deliver insights that assist clients in making informed decisions and managing risks. Unlike many competitors, S&P Global has a diverse range of divisions, including S&P Global Ratings and S&P Global Market Intelligence, which allows it to cater to various financial needs. The company's goal is to support clients in driving growth while also committing to corporate responsibility and making a positive impact on society and the environment.

Company Stage

IPO

Total Funding

N/A

Headquarters

New York City, New York

Founded

N/A

Growth & Insights
Headcount

6 month growth

16%

1 year growth

3%

2 year growth

16%
Simplify Jobs

Simplify's Take

What believers are saying

  • S&P Global's strategic acquisitions and investments, such as in Novata, demonstrate a strong commitment to expanding sustainability solutions and enhancing their service offerings.
  • The launch of the India Research Chapter highlights S&P Global's focus on emerging markets, potentially leading to significant growth opportunities.
  • Their diverse revenue streams, including subscription-based models and transaction services, provide financial stability and resilience against market fluctuations.

What critics are saying

  • The integration of acquired companies like Visible Alpha may pose challenges in aligning technologies and cultures, potentially affecting service delivery.
  • The competitive landscape in financial analytics is intense, with rivals like Bloomberg and Moody's potentially eroding S&P Global's market share.

What makes S&P Global unique

  • S&P Global's comprehensive suite of services, including credit ratings, market intelligence, and indices, positions it as a one-stop solution for financial information and analytics, unlike competitors who may specialize in only one area.
  • Their commitment to corporate responsibility and ESG solutions sets them apart in the financial sector, appealing to clients focused on sustainable and ethical investing.
  • The acquisition of companies like Visible Alpha and World Hydrogen Leaders enhances S&P Global's capabilities in investment research and commodity insights, providing a competitive edge.

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