Description :

The Capital IQ Solutions Data Science team supports the S&P Capital IQ Pro platform 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 and engineers 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 :

- Design solutions utilizing NLP models including chat assistants and RAG systems.

- Design and develop custom NLP LLM Models including both prompt engineering techniques and model fine-tunning and alignment (SFT, RLHF, DPO)

- NLP Model evaluation using both human-supported and synthetic evaluation methods and metrics.

- Deploy NLP models ensuring latency, reliability, and scalability.

- Discover new methods for prompt engineering, model fine-tuning, quantization and latency optimization, document embeddings and chunking.

- Collaborate closely with product teams, business stakeholders, and engineers to ensure smooth integration of NLP models into production systems.

- Troubleshoot complex issues related to machine learning model development and data pipelines and develop innovative solutions.

- Actively research, explore and identify the latest relevant methods and technologies.

What Were Looking For :

Basic Required Qualifications :

- 6-10 years of professional experience in Advanced Analytics / Data Science / Machine Learning

- Degree in Computer Science, Mathematics or Statistics, Computational linguistics, Engineering, or a related field.

- Good understanding of machine learning and deep learning methods and their mathematical foundations

- 5-9 years hands-on experience developing NLP models, ideally with transformer architectures.

- Demonstrated experience with Python, PyTorch, Hugging Face or similar tools.

- Mastery of Python and ability to write robust and high standard, testable code

- Knowledge of developing or tuning LLMS.

Additional Preferred Qualifications :

- 3+ years of experience with implementing information retrieval systems.

- Experience with contributing to Open-Source initiatives or in research projects and/or participation in Kaggle competitions.

- Publications related to Machine Learning or Deep Learning

- Ability to work in a team

- Able to report progress and summarize issues to a less technical audience

- Curious and open-minded attitude to new approaches.