Associate Director - Data Science Rag, Nlp, Llm And Genai
- S&P Global Market
- 8 - 13 years
- Hyderabad
- 3 months ago
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Job Description
The Role: Associate Director of Data Science RAG, NLP, LLM and GenAI
S&P is a leader in risk management solutions leveraging automation and AI/ML. This role is a unique opportunity for hands-on ML scientists and NLP/Gen AI/ LLM scientists to grow into the next step in their career journey and apply her or his technical expertise in NLP, deep learning, GenAI, and LLMs to drive business value for multiple stakeholders while conducting cutting-edge applied research around LLMs, Gen AI, and related areas.
Responsibilities and Impact:
- 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 Were Looking For:
Basic Required Qualifications:
- Bachelor's / Masters or Ph.D degree in Computer Science, Mathematics or Statistics, Computational linguistics, Engineering, or a related field.
- 8+ 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, RAG, and NLP.
- Demonstrated 3+ years hands-on experience with Python, Hugging Face, TensorFlow, Keras, PyTorch 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.
Job Classification
Industry: BankingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Head - Data ScienceEmployement Type: Full timeContact Details:
Company: S&P Global MarketLocation(s): Hyderabad
Keyskills: python natural language processing tensorflow pytorch keras github c++ aiml machine learning artificial intelligence sql deep learning r java data science predictive modeling model development statistical modeling aws ml statistics
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