Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Design and Product Analytics
Using quantitative metrics to inform product and design decisions. Covers key user engagement metrics such as conversion rates, task completion, retention, and feature adoption, and how to instrument and interpret these signals using analytics platforms and product dashboards. Explains how quantitative data complements qualitative research, how to identify design problems from metrics, design experiments and metrics for validation, and how to translate findings into design priorities and success criteria.
Analytical Background
The candidate's approach to analytical, evidence-based problem solving: how they take an ambiguous question, break it into testable pieces, gather and examine relevant information or data, choose appropriate methods to reach a conclusion, and turn that conclusion into a concrete recommendation or decision. This can show up as quantitative work (statistics, data analysis, experimentation, dashboards) or as qualitative and domain-specific analysis (reviewing logs or incidents, case or contract research, market or process analysis, root-cause investigation). Draw on academic projects, internships, or professional work. Focus on the end-to-end path: how the question or hypothesis was framed, what evidence was examined and with what tools or methods, what trade-offs were considered, and how the resulting insight changed a real decision or outcome.