Associate Director of Data Scientist – NLP, LLM and GenAI

Key Skills

Aartificial intelligence, Data science techniques, Generative AI, HuggingFace, Keras software library, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP), Python Programming, PyTorch, TensorFlow

Job Description

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.

What We’re Looking For:

• Bachelor's / Master’s or Ph.D degree in Computer Science, Mathematics or Statistics, Computational linguistics, Engineering, or a related field. 
• 4+ 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 3+ years hands-on experience with Python, Hugging Face, TensorFlow, Keras, PyTorch, Spark or similar statistical tools. Expert in python programming.
• 3+ years hands-on experience developing natural language processing (NLP) models, ideally with transformer architectures. 
• 3+ 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 and measurable hands-on experience with developing or tuning Large Language Models (LLM) and Generative AI (GAI)
• Experienced with NLP, LLMs (extractive and generative), fine-tuning and LLM model development. Strong familiarity 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.

Compensation/Benefits Information (US Applicants Only):

S&P Global states that the anticipated base salary range for this position is $150,000 - $215,000. Final base salary for this role will be based on the individual’s geographical location as well as experience and qualifications for the role.

In addition to base compensation, this role is eligible for an annual incentive plan. This role is not eligible for additional compensation such as an annual incentive bonus or sales commission plan.

This role is eligible to receive additional S&P Global benefits. For more information on the benefits we provide to our employees, please click here.