Data Scientist

Note: Clearance: An interim clearance is sufficient for the start of this position. Onsite Requirement: This position is HYBRID. The individual will be required to be onsite 3 days a week. Candidates are expected to be local at the time of the start of the assignment. Final scheduled TBD by hiring manager. Description: Designs, develops, and implements methods, processes, and systems to consolidate and analyze diverse data sets including structured and unstructured. Develops software programs, algorithms, dashboards, information tools, and queries to clean, model, integrate and evaluate datasets. Keeps abreast of new analytic methodologies and technologies. Collaborates with functional business units to drive business solutions and direction. Group develops human-centered technologies to overcome operational challenges and to enhance human capability in domains of interest to national security. Our team is seeking a candidate with expertise in Generative AI to join our research and development group, focused on advancing machine learning solutions for large-scale text processing. In this role, you will collaborate with interdisciplinary teams to design, implement, and optimize range of AI/ML/NLP/GenAI processes. You will apply your expertise in machine learning algorithms, data science, and high-performance computing environments(e.g., supercomputers) to create robust, scalable AI applications that address complex language learning-related challenges Summary: Group is looking for an Research Scientist who Develops and implements advanced machine learning and generative AI algorithms. Designs and optimizes scalable data pipelines and AI workflows. Evaluates algorithms for correctness, performance, and robustness, including unit tests and validation against truth data. Optimizes algorithms for efficiency, scalability, and computational complexity. Applies data science, model fine-tuning, and responsible AI principles, including fairness and ethics. Evaluates, designs and implements infrastructure for experimentation on data and process tracking. Collaborates with interdisciplinary teams to integrate AI technologies and solutions. Stays current on emerging technologies and innovations for AI/ML pipelines. Documents development processes and presents research findings to stakeholders. Key Responsibilities: • Develop and optimize state-of-the-art generative AI models for foreign language proficiency testing and education tasks. Use and extend state of the art NLP techniques including large language model fine-tuning, natural language understanding, and content generation. • Design and implement scalable algorithms and pipelines to handle extensive text datasets in high-performance computing (HPC)environments. • Collaborate with software engineers, data scientists, and domain experts to delivered-to-end AI solutions for a variety of text-centric applications. • Conduct research on novel methodologies, staying current with emerging trends in machine learning, generative AI, and NLP. • Present technical findings to both expert and non-expert audiences, providing clear explanations of complex concepts and proposed solutions. Minimum Qualifications: • Master’s degree (or equivalent professional experience) in Computer Science, Math, AI/ML, Data Science, Statistics, Applied Linguistics, or a related field. • Proficiency in Python, with demonstrable experience developing and implementing algorithms for AI/ML workflows. • Strong background in natural language processing, machine learning, and generative AI, with a portfolio of relevant coursework, publications, or project work. • Hands-on experience with data science pipelines, including data cleaning, preparation, and visualization, for large-scale text corpora. • Familiarity with high-performance computing (e.g., supercomputers, clusters, or cloud infrastructure) and the associated challenges of large-scale AI model development. Preferred Qualifications: • Experience with deep learning frameworks for building and training complex human language technology systems. • Familiarity with responsible AI principles, model interpretability, and ethical considerations in large language model deployment. • Proven experience collaborating on shared code repositories (e.g., Git),contributing to communal software projects or technical communities. • Enthusiasm for learning new technologies and exploring innovative applications of generative AI. • (Bonus) Demonstrated interest in learning foreign languages