Lead Data Scientist

The CRISIL Data Science team supports the S&P Global with innovative Data Science and Machine Learning solutions, utilizing the most advanced NLP Generative AI models. This role presents a unique opportunity for hands-on ML/NLP/Gen AI/LLM scientists to advance to the next step in their career journey and apply their technical expertise in NLP, deep learning, Gen AI, and LLMs to drive business value for multiple stakeholders while conducting cutting-edge applied research in LLMs, Gen AI, and related areas. 

Responsibilities and Impact:

- Develop models, algorithms and data pipelines that leverage wealth of S&P Global data (structured and unstructured) to provide actionable insights to clients
- Lead data science product development start to end from PoC to full productization - Actively research, explore and identify the latest relevant techniques - Identify opportunities for innovation across the Ratings division - Provide subject matter expertise to internal stakeholders, advice and guide on model and data suitability

What We’re Looking For:  

- 6+ years of professional experience in Advanced Analytics / Data Science / Machine Learning
- Hands on project lifecycle experience, from business requirements gathering to productization - 8+years in Coding experience in Python to write robust, reusable code; experience with version control (preferably GitHub); experience with writing test cases for data science pipelines; - 3+ years in Experience working with databases and SQL - 2+ years in Experience using Language Models to solve a range of predictive task involving text - Ability to quickly acquire new technical skills - 6+ years in Experience of working with large data sets and distributed computing - Good understanding of mathematical foundations of Machine Learning models - Able to translate business problems into problems that can be solved with Data Science - 2+ years experience in developing RAG-based models without the aid of any frameworks - 3+ years experience in Prompt Engineering

Preferred Qualifications:

- Good familiarity with recent developments in GenAI and LLMs
- STEM/Econometrics / Financial Engineering background - Cloud (AWS and Azure) - Experience with model visualization and explainability - Published atleast 1 journal paper or conference paper or patent