We are building an advanced AI platform focused on automating and enhancing tax-related processes across global finance organizations. The project leverages Large Language Models (LLMs), Generative AI, and Deep Learning to support consolidation, reconciliation, reporting, and interpretation of complex, unstructured tax documentation.
As a Senior Data Scientist, you will play a key role in designing, developing, and deploying scalable AI solutions that meet regulatory, legal, and performance requirements in cloud-based environments.
responsibilities :
Collaborate with software engineers, data scientists, and business analysts to gather requirements, refine AI models, and integrate LLM-based solutions into enterprise platforms
Design and implement Generative AI and LLM solutions tailored to tax-specific use cases
Apply RLHF (Reinforcement Learning from Human Feedback) and advanced optimization techniques to improve model outputs
Embed AI workflows into consolidation, reconciliation, and financial reporting processes
Develop and deploy Deep Learning models for NLP and GenAI use cases
Leverage LLMs to analyze and interpret unstructured tax and legal documentation
Preprocess and prepare raw data, including text normalization, tokenization, and feature extraction for NLP models
Set up, train, fine-tune, and evaluate Large Language Models and other state-of-the-art neural networks
Conduct thorough testing, validation, and statistical analysis to ensure accuracy, robustness, and reliability
Optimize model performance across various computational environments, including cloud and edge platforms
Monitor, maintain, and scale generative models in production environments
Perform model audits to identify risks, biases, and compliance gaps
Partner closely with tax, finance, IT, and legal teams to ensure regulatory compliance and successful AI adoption
Research emerging trends in Generative AI and apply cutting-edge techniques to ongoing projects
Propose innovative AI-driven use cases to enhance tax and finance functions
requirements-expected :
6+ years of hands-on experience in Data Science or Applied Machine Learning
Strong programming skills in Python, with experience using PyTorch, TensorFlow, and related ML libraries
Solid understanding of object-oriented design patterns, concurrency/multithreading, and scalable AI model deployment
Proven experience in developing, training, and fine-tuning LLMs and advanced AI models
Practical knowledge of Deep Learning architectures and techniques, including CNN, RNN, LSTM, GANs, Transformers, RAG, and LangChain
Expertise in Prompt Engineering and working with vector databases
Proficiency in NLP tools and techniques, including RegEx, SpaCy, NLTK, semantic extraction, and text representation
Experience with Vector Databases (e.g., Milvus, PostgreSQL) and general database technologies
Familiarity with Azure Cloud Platform and cloud-native AI solutions
Hands-on experience with Docker, Kubernetes, and CI/CD pipelines
Strong analytical skills with the ability to perform statistical evaluation and model optimization
