Senior Data Scientist

We are seeking a highly skilled and innovative Senior Data Scientist to join our advanced analytics and machine learning team. The ideal candidate will bring deep expertise across a range of AI/ML disciplines, including deep learning, classical machine learning, natural language processing (NLP), and Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs). You will be responsible for building and deploying cutting-edge models that solve real-world problems in complex data environments. Key Responsibilities: Lead development of predictive models using deep learning and classical machine learning algorithms. Build and optimize NLP models leveraging transformer-based architectures (e.g., BERT, GPT). Design and implement Retrieval-Augmented Generation (RAG) approaches for LLM-based applications. Work with vector databases and graph databases for knowledge representation and retrieval. Generate and use simulated data to support training and testing of ML models. Perform data standardization, aggregation, and integration from structured and unstructured sources. Collaborate with cross-functional teams to understand business needs and translate them into ML solutions. Document workflows, produce technical reports, and contribute to academic or industry publications. Use version control tools to manage collaborative model development (e.g., GitHub). Support end-to-end ML lifecycle from data wrangling and modeling to deployment and monitoring. Required Qualifications: Minimum 8 years of hands-on experience in AI/ML software development using Python and R. Strong background in deep learning, transformer-based NLP, and classical ML. Experience with RAG, LLMs, and embedding-based retrieval systems. Expertise in data wrangling, data standardization, and feature engineering. Proven experience working with vector databases (e.g., FAISS, Pinecone, Weaviate) or graph databases (e.g., Neo4j). Strong understanding of ML frameworks such as TensorFlow, PyTorch, and scikit-learn. Familiarity with version control systems (e.g., Git, GitHub). Experience with cloud computing platforms (AWS, GCP, or Azure). Demonstrated ability to produce high-quality technical documentation and research publications. Master’s degree or Ph.D. in Statistics, Computer Science, or a related quantitative discipline. Preferred Qualifications: Hands-on experience with SAS, SQL, Amazon RDS, and JIRA. Knowledge of ML Ops practices and model deployment pipelines. Prior experience in contributing to academic or technical publications. Exposure to federal or enterprise-scale projects is a plus. Job Types: Full-time, Contract Pay: $160,000.00 - $180,000.00 per year Benefits: 401(k) Dental insurance Health insurance Paid time off Vision insurance Work Location: Hybrid remote in Herndon, VA 20170