Data Scientist / Engineer

Required experience

• Demonstrated experience applying data science techniques — computer programming, mathematics and statistics, machine learning, graph analytics, and data analysis — to extract insights from diverse data types (e.g. imagery, geospatial, language, economic, and scientific).
• Demonstrated experience processing, analyzing, and reformatting large-scale tabular- and graph-structured data, from single files to multi-terabyte datasets.
• Demonstrated experience developing, training, testing, and evaluating AI/ML models for tasks such as object detection, data triage, search and optimization, inference, and automated decision-making.
• Demonstrated experience with data labeling and tagging tools and methods that maximize the accuracy, reusability, and explainability of training datasets.
• Demonstrated experience designing, building, and maintaining databases, data pipelines (extract, transform, load), and large-scale data processing systems.
• Demonstrated experience creating software pipelines to query, combine, and merge data sources for analysis.
• Demonstrated experience maintaining dataset versioning to ensure model reproducibility.
• Demonstrated ability to communicate analytic results to both technical and non-technical stakeholders.

Technologies

The contractor team shall have experience with the following technologies, including but not limited to:

• Python
• SQL / NoSQL databases
• Anaconda
• Jenkins
• GitHub
• JIRA
• TensorFlow / PyTorch
• Cloud (VPC) environments

Highly desired

The following experience is highly desired, though not required.

• Demonstrated experience developing dashboards and custom static or interactive data visualizations and prototype web applications.
• Demonstrated experience deploying analytical software packages and custom virtual environments in standalone, on-premises, or cloud environments.
• Demonstrated experience identifying and mitigating AI/ML model vulnerabilities.
• Demonstrated experience providing data engineering support that maximizes data scientists' time on mission.
• Demonstrated experience with cloud security and the assessment and authorization (A&A) of data science compute environments.
• Demonstrated experience creating, facilitating, or delivering data science training (courses, mentoring, or hackathons).