Senior Data Scientist

Job Summary: The Senior Data Scientist will coordinate complex cloud and artificial intelligence initiatives that leverage large language models and advanced machine learning within a hybrid work model. The role will orchestrate projects across AWS, GCP, or Azure environments, ensuring reliable Python based solutions that apply frameworks such as Keras, spaCy, and PyTorch to deliver measurable value for clients and communities.

Required Skills: Python, PyTorch, Machine Learning, Prompt Engineering, Large Language Models, AWS / GCP / Azure Cloud

Responsibilities:
Develop and implement LLM-based applications tailored for in-house legal needs, ensuring they align with Cognizant'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 Cognizant s values in every task. 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 modelling 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. nal environments where programs span different regions, cultures, and regulatory contexts within a day shift structure