The multinational professional services network is seeking an AI Data Scientist, Sr. Manager to lead AI initiatives focused on integrating generative AI and large language models into tax functions, enhancing processes such as consolidation, reconciliation, and reporting.
Requirements
- 8+ years of programming experience in Python, PyTorch, TensorFlow, and related libraries
- Solid understanding of object-oriented design patterns, concurrency/multithreading, and scalable AI and GenAI model deployment
- Proficiency in RegEx, Spacy, NLTK, and NLP techniques for text representation and semantic extraction
- Hands-on experience in developing, training, and fine-tuning LLMs and AI models
- Practical understanding and experience in implementing techniques like CNN, RNN, GANs, RAG, Langchain, and Transformers
- Expertise in Prompt Engineering techniques and various vector databases
- Familiarity with Azure Cloud Computing Platform (Preferred)
Responsibilities
- Lead a team of engineers, data scientists, and business analysts to understand requirements, refine models, and integrate LLMs into AI solutions
- Incorporate RLHF and advanced techniques for tax-specific AI outputs
- Embed generative AI solutions into consolidation, reconciliation, and reporting processes
- Leverage LLMs to interpret unstructured tax documentation
- Development and implementation of Deep learning algorithms for AI solutions
- Preprocess raw data, including text normalization, tokenization, and other techniques, to make it suitable for use with NLP models
- Setup and train large language models and other state-of-the-art neural networks
Other
- Ability to perform job responsibilities within a hybrid work model that requires US Tax professionals to co-locate in person 2-3 days per week
- Ability to travel 20%, on average, based on the work you do and the clients and industries/sectors you serve
- Bachelor's degree in computer science, Engineering, or a related field
- Experience with Docker, Kubernetes, CI/CD pipelines (Preferred)
- Experience with Deep learning, Computer Vision, CNN, RNN, LSTM (Preferred)
