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 analytical skills and experience with data driven problem solving, including statistics, data analysis projects, tools and languages used, and examples of insights that influenced product or business decisions. This covers academic projects, internships, or professional analytics work and the end to end approach from hypothesis to measured result.
Experimentation Metrics and Strategy
Designing experiments and selecting appropriate primary, secondary, and guardrail metrics to evaluate hypotheses while protecting long term user value. This includes choosing metrics that reflect both short term signal and long term outcomes, reasoning about metric interactions and potential unintended consequences, and applying statistical considerations such as minimum detectable effect, sample size and power analysis, test duration, and external validity across segments and platforms. Candidates should also discuss experiment risk mitigation, stopping rules, and how to operationalize experiment results into product decisions.
Business Intelligence Background
A summary of business intelligence experience including the BI platforms and tools used, types of dashboards and reports built, data volumes and sources, analytical methods, stakeholder consumption patterns, and measurable business outcomes. Candidates should explain how BI efforts influenced decisions, examples of ETL or modeling work, and any leadership or ownership of BI initiatives.
Data and Trend Analysis with Pattern Recognition
Analyzing quantitative and qualitative data to identify patterns, trends, correlations, and meaningful insights. Skills assessed include descriptive statistics, time series and trend analysis, visualization and dashboarding, hypothesis generation and testing, identifying seasonality and structural changes, distinguishing signal from noise, and synthesizing findings into clear recommendations. For qualitative inputs candidates should demonstrate coding, theme extraction, categorization, and synthesis of transcripts or survey responses. Emphasis is on choosing appropriate methods, validating patterns, avoiding common pitfalls such as confounding and spurious correlation, and communicating insights effectively to stakeholders.