Sr. Data Scientist

W2 only No C2C

Position : Sr. Data Scientist

Location: Charlotte NC Hybrid (4 days onsite and 1 day remote)

Duration: 6 Months Contract

Job Description:

Data Engineering & Data Processing:

• Design and develop scalable ETL/ELT pipelines for ingesting transforming and processing structured and unstructured data.
• Build and optimize data pipelines using Databricks Spark SQL and cloud-native AWS services.
• Implement data quality validation lineage and monitoring processes.
• Support medallion/Lakehouse architecture patterns including bronze silver and gold data layers.
• Develop data pipelines to support AI/ML GenAI and RAG workloads including document ingestion and embedding generation workflows.

Machine Learning & Modeling:

• Design and implement scalable ML models for classification regression clustering forecasting and recommendation systems.
• Apply advanced techniques including deep learning ensemble learning NLP Generative AI and LLM-based solutions where applicable.
• Conduct model evaluation tuning validation and performance optimization using industry best practices.
• Develop and train models within Databricks ML and/or AWS SageMaker leveraging distributed computing and scalable cloud infrastructure.
• Build reusable feature engineering and model training pipelines.
• Develop Retrieval-Augmented Generation (RAG) solutions integrating LLMs with enterprise knowledge sources and vector databases.

Cloud & MLOps:

• Deploy and manage ML and GenAI models using AWS SageMaker and Databricks including endpoint configuration monitoring and retraining workflows.
• Utilize Databricks MLflow for experiment tracking model registry and deployment automation.
• Implement and support vector database solutions for semantic search and RAG architecture.
• Collaborate with DevOps and platform teams to implement CI/CD pipelines for ML GenAI and data workloads.
• Automate operational workflows and optimize cloud resource utilization scalability reliability and security.

Deliverables:

• Production-ready ML and GenAI solutions with supporting technical documentation.
• Scalable ETL/ELT pipelines and curated datasets.
• End-to-end Databricks notebooks jobs and workflows.
• Feature engineering pipelines and reusable ML components.
• RAG pipelines integrated with vector databases and enterprise knowledge sources.
• Weekly status reports and participation in Agile sprint ceremonies.

Skills & Qualifications:

• 8 years of experience in Data Science Machine Learning and Data Engineering.
• Strong proficiency in Python SQL Spark and ML libraries such as scikit-learn TensorFlow and PyTorch.
• Experience with Generative AI LLM frameworks prompt engineering and RAG architecture.
• Hands-on experience with vector databases and semantic search technologies.
• Hands-on experience with Databricks MLflow Delta Lake and AWS SageMaker.
• Experience designing scalable data pipelines and distributed data processing solutions.
• Strong understanding of data mining feature engineering and data modeling techniques.
• Experience with cloud-native AWS data services and orchestration frameworks.
• Excellent communication collaboration and leadership skills.