Data Scientist - Mentorship and Training (AI/ML)

Swift•McLean, VA

3d•Onsite

About The Position

Swift is seeking a high-performing Data Scientist to combine hands-on data science with leading training modules and mentoring data scientists. The role involves designing and delivering onboarding and upskilling programs, teaching complex topics, and establishing best practices for data science teams. The ideal candidate is passionate about turning multi-source data into clear insights and training others to do the same.

Requirements

BS/MS in Computer Science, Data Science, Statistics, or a related field, or equivalent experience
Minimum 10 years of data science experience
Demonstrated strong research skills, especially in the areas of emerging AI/ML technologies
Ability to reverse engineer AI/ML models
Demonstrated understanding of data science, AI/ML in the context of IC missions
Advanced Python proficiency
Expertise writing complex, performant SQL across multiple data sources; comfortable with schema exploration and query optimization
Expertise with Git-based workflows (branches, PRs, code reviews)
Proficiency with Linux fundamentals (shell, filesystem, SSH), Conda environment management
Fluency with Jupyter Notebooks for exploration, demos, and reproducible training assets
Demonstrated ability to debug code and data issues
Exceptional communication skills and a coaching mindset; able to explain technical concepts clearly to junior data scientists in visual, verbal, and written formats
Proven experience collaborating with multiple stakeholders and incorporating feedback into iterative improvements
Desire to learn new data science techniques or software and prepare trainings
US citizenship and an active TS/SCI with Polygraph security clearance required.

Nice To Haves

Experience developing new training modules from scratch, benchmarking skills needed in collaboration with stakeholders
Elastic Stack familiarity (Elasticsearch indexing/search patterns, Kibana visualizations, Logstash/Filebeat pipelines).
Apache Spark (PySpark) for large‑scale data processing and performance tuning.
FastAPI for building well‑documented, testable services that expose models and analytics.
Docker and microservices fundamentals
ETL/ELT design patterns and data workflow orchestration
Sphinx for generating maintainable, versioned documentation from source.
Understanding of API security and certificates
Experience with client’s Data Science environment
Knowledge and implementation of Retrieval-Augmented Generation (RAG) and LLMs
Experience with web development through Gradio, Streamlit, etc.

Responsibilities

Design and deliver onboarding and upskilling programs for data scientists.
Design and teach/facilitate in-person trainings to include full day classroom trainings and 1–2-hour deskside trainings
Author and refine training materials and playbooks that make complex topics approachable.
Establish and champion best practices for version control (Git/GitFlow), code review, and continuous integration for data science teams.
Document tools, packages, and methodologies with Sphinx.
Partner with stakeholders to translate mission needs into training, tooling improvements, and backlog priorities.