Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
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.
Trend Analysis and Anomaly Detection
Covers methods for detecting and interpreting deviations in metric behavior over time and determining whether changes reflect real product or user behavior versus noise. Topics include baseline establishment, seasonality and holiday effects, time series decomposition, smoothing and aggregation choices, statistical detection techniques such as control charts, z scores, EWMA and CUSUM, thresholding strategies, and modern algorithmic approaches like isolation forest or LSTM-based detectors. Also covers visualization and dashboarding practices for communicating trends, setting sensible alerting rules, triage workflows for investigating anomalies, and assessing business impact to prioritize fixes or rollbacks.