Senior Data Scientist

IQUASAR LLC

1d•Hybrid

About The Position

iQuasar is seeking to fill a Data Scientist position in Reston, VA. This is a permanent, full-time role that is 90% remote with 10% required travel to Brite's office in Reston, VA. The company focuses on providing cutting-edge technologies and offers competitive compensation and benefits, including health, vision, and dental insurance, a matching 401k plan, excellent training, and a vibrant working environment. Employees are expected to be exceptional and contribute to innovation with a strong sense of mission and integrity.

Requirements

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, Web scrapping, 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.
Secret Clearance

Nice To Haves

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.

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.