Senior Data Scientist

Role: Sr Data Scientist Location: Raleigh NC - On-Site Type: Contract C2C/W2 Overall Experience: 12+ Years Must Interview Process: 3 Round with Coding task RESPONSIBILITIES • Develop and implement LLM-based applications tailored for in-house legal needs, ensuring they align with client’s commitment to excellence and innovation • Evaluate and maintain our data assets and training/evaluation datasets, ensuring they meet the highest standards of integrity and quality. • Design and build pipelines for preprocessing, annotating, and managing legal document datasets, fostering a customer-centric mindset. • Collaborate with legal experts to understand requirements and ensure models meet domain-specific needs, working as one team. • Conduct experiments and evaluate model performance to drive continuous improvements, raising the bar in all deliverables. • Evaluate AI/ML and GenAI outcomes, both human and automated, to ensure accuracy, reliability, and alignment with business objectives. • Interface with other technical personnel or team members to finalize requirements, demonstrating ownership of outcomes. • Work closely with other development team members to understand complex product requirements and translate them into software designs, ensuring ethical choices in all actions. • Successfully implement development processes, coding best practices, and code reviews for production environments, embodying client’s values in every task. REQUIREMENTS • Strong hands-on experience and foundations in machine learning, including dimensionality reduction, clustering, embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of AWS, GCP, Azure, or other cloud platforms. • Understanding of data modeling principles and complex data models. • Proficiency with relational and NoSQL databases as well as vector stores (e.g., Postgres, Elasticsearch/OpenSearch, ChromaDB). • Knowledge of Scala, Spark, Ray, or other distributed computing systems is highly preferred. • Knowledge of API development, containerization, and machine learning deployment is highly preferred. • Experience with ML Ops/AI Ops is highly preferred.