Tools, Frameworks & Implementation Proficiency Topics
Practical proficiency with industry-standard tools and frameworks including project management (Jira, Azure DevOps), productivity tools (Excel, spreadsheet analysis), development tools and environments, and framework setup. Focuses on hands-on tool expertise, configuration, best practices, and optimization rather than conceptual knowledge. Complements technical categories by addressing implementation tooling.
AI Assisted Coding Practices
Evaluation of how a candidate uses AI based developer tools safely and effectively. Topics include writing clear prompts, verifying and testing generated code, debugging and reasoning about AI suggestions line by line, identifying hallucinations or incorrect assumptions in generated code, integrating AI assistance into a reproducible workflow, and knowing when manual implementation or deeper review is required. Candidates should be able to show how they validate AI outputs and maintain code quality.
Research Platform and Tools Architecture
Selecting and integrating research platforms and tools to support qualitative and quantitative research workflows. Topics include evaluation of survey platforms, usability testing tools, qualitative analysis systems, participant management, data storage and security for research data, and integration points with analytics and reporting systems. Candidates should demonstrate the ability to match tools to research goals, consider cost and complexity, and design architectures that preserve data privacy and support analysis.
Experiment Tracking and Reproducibility
Focuses on the tools, processes, and engineering practices that ensure experiments can be reproduced, audited, and compared over time. Areas include systematic logging of hyperparameters and results, experiment metadata and registries, code and model version control, dataset versioning and provenance, environment and dependency capture, artifact and checkpoint management, deterministic training practices and random seed handling, automation of experiment pipelines, and integration with continuous integration systems. Candidates should be able to discuss common reproducibility pitfalls, strategies for enabling large scale experiment comparison and analysis, and how experiment artifacts support knowledge reuse and evidence based decision making.
Python for Research
Proficiency in Python or another primary research language for implementing experiments and prototypes. Topics include writing idiomatic and readable code, using scientific libraries such as NumPy, SciPy, pandas, and scikit learn, numerical considerations and vectorized operations, testing, reproducibility and experiment automation, packaging and dependency management, and performance debugging and profiling in a research workflow.