• Anomaly detection using deep neural networks
• Numerical optimization applied to problems in manufacturing
• Personal identifiable information (PII) and personal health information (PHI) detection in natural language
• Understanding how to frame business problems as data science problems
• Navigating the full data science lifecycle: research and exploration, development, deployment, support
• Using correct model selection and training procedures to prevent common modeling errors
• Creating reasonable test scenarios and diagnostic measures
• Writing high quality Python (readable, reusable, modular, and well-abstracted code)
• Using Linux, the terminal, and other core development tools
• Understanding source control (git), containerization (docker) and deployment technologies (cicd/k8s)
• Participating in code reviews
• Contributing to team development processes and agile/scrum rituals
• Helping to clarify and prioritize work, define success criteria, and own work item completion
• Applying all relevant change management controls for risk and compliance policies
• Presenting work clearly to technical and non-technical audiences