Research Scientist Guide
Comprehensive Research Scientist Interview Notes and Guides
Preface
This is a curated collection of Research Scientist interview notes and guidelines. It is designed to serve as a practical reference for candidates preparing for research-oriented roles in academia, industry, and applied labs.
The materials consolidate insights from research experiences and professional practice to cover both technical depth and practical preparation. Each section begins with key learning objectives, followed by structured notes, example problems, and additional resources. The notes highlight essential methods, provide applied examples, and offer interview strategies. They are not intended to replace textbooks or formal training, but rather to complement them with targeted guidance.
The intended audience is expected to have foundational knowledge in computer science, machine learning, and statistics, including:
- Foundations in Statistics & Mathematics
- probability and statistics
- hypothesis testing and inference
- regression and correlation analysis
- probability and statistics
- Algorithms & Optimization
- algorithms and data structures (sorting, searching, graph, dynamic programming, etc.)
- optimization techniques (convex, non-convex, gradient-based methods)
- Machine Learning & AI
- classical and modern machine learning methods
- reinforcement learning and deep learning foundations
- natural language processing (NLP) and large language models (LLMs)
- AI/LLM design patterns (retrieval-augmented generation, agentic workflows, tool-use, prompt engineering, safety & alignment strategies)
- Research Methods
- research methodology and experimental design
- critical analysis and interpretation of results
Citation
Kumar, Kumar. (2025). Research Scientist Interview Notes and Guidelines. Edition 2025-10.
License
This work is licensed under the MIT License.