Sr. Data Scientist - Charlotte, NC - Onsite

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.