Data Scientist - Mentorship and Training
Summary
The Data Scientist will design and deliver training programs, mentorship, and technical documentation for data science teams while maintaining hands-on development work. The role requires advanced Python and SQL skills, experience with Git workflows, and an active TS/SCI with Polygraph security clearance.
- 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.
- BS/MS in Computer Science, Data Science, Statistics, or a related field, or equivalent experience
- Minimum 10 years of data science experience
- 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.
- 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.
