Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Business Impact Measurement and Metrics
Selecting, measuring, and interpreting the metrics that show whether an initiative, product, or program actually delivered value, and using that evidence to guide decisions. Covers headline outcome metrics (revenue decomposition, customer lifetime value, churn and retention, average revenue per user, unit economics and cost per transaction) alongside operational indicators (throughput, quality, reliability) and how to connect the two. Candidates should be able to distinguish leading from lagging indicators, map operational metrics to business outcomes, form and test hypotheses about what is driving a metric, choose an evaluation window, and recommend changes to what gets measured. Also covers the fundamentals of establishing a valid baseline and comparison group (before/after checks, A/B tests, and other quasi-experimental comparisons when a controlled test is not possible), reasoning about whether an observed change is large enough and reliable enough to act on, and ruling out obvious confounding explanations. Includes quick back-of-the-envelope estimation for order-of-magnitude impact, translating technical or operational metrics into business consequences, building a simple health dashboard for a program or initiative, and communicating results (including uncertainty) as a clear, decision-ready narrative for stakeholders. Depth and specific techniques (for example difference-in-differences, regression discontinuity, or survival analysis) should scale to the role: some interviews probe rigorous experimental design, others probe sound judgment using simpler before/after comparisons.
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.
Metrics and KPI Fundamentals
Core principles and practical fluency for defining, measuring, and interpreting metrics and key performance indicators, applicable across any professional domain. Candidates should be able to select meaningful metrics aligned to business objectives rather than vanity metrics, explain the difference between a metric and a target, and distinguish leading indicators from lagging indicators. Coverage includes decomposing complex outcomes into actionable component metrics, writing precise metric definitions (for example what counts as an active user, a completed case, a qualified lead, or a resolved ticket, depending on the domain), calculating common rate-based metrics such as engagement rate, churn rate, conversion rate, cycle time, or utilization rate, establishing baselines and sensible targets, and interpreting signal versus noise including awareness of statistical variability. Also includes using segmentation and cohort analysis to diagnose metric movements, and recommending two to three meaningful metrics for a hypothetical problem in the candidate's own domain with justification and action plans.