Senior Data Scientist

TechDigital Group; Raleigh (NC); On-site; USD 110,000 - 145,000; Full time

Job summary

TechDigital Group is seeking a Senior Data Scientist to lead cloud and AI initiatives. This role involves leveraging large language models and machine learning within a hybrid work model. Ideal candidates will have experience across AWS, GCP, or Azure, ensuring Python-based solutions deliver measurable value.

The position requires strong expertise in machine learning frameworks and collaboration with legal experts to meet domain-specific needs, ensuring all implementations align with ethical standards.

Qualifications

• 5+ years experience in data science or related field.
• Strong knowledge of machine learning and deep learning concepts.
• Experience with natural language processing and large language models.

Responsibilities

• Develop and implement LLM based applications for legal needs.
• Evaluate and maintain data assets for quality.
• Design and build pipelines for managing legal datasets.

Skills

Python

PyTorch

Machine Learning

Prompt Engineering

Large Language Models

AWS / GCP / Azure Cloud

Tools

spaCy

TensorFlow

Hugging Face Transformers

Job description

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 Cognizants 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