Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Compensation Data Analysis and Interpretation
Demonstrate a structured approach to analyzing compensation datasets and drawing business insights. Include exploratory data analysis steps such as distribution checks outlier detection group aggregation and cross tabulation; data cleaning and validation practices; use of structured query language and advanced spreadsheet techniques to compute means medians percentiles variances and other summary statistics; and application of regression or other controls to isolate pay drivers in pay equity work. Emphasize documenting assumptions producing clear visuals and translating technical findings into actionable recommendations for HR and business partners.
Market Benchmarking and Salary Positioning
Approaches to analyzing market compensation survey data and positioning internal pay against market benchmarks. Topics include mapping internal roles to survey roles normalizing for geography and currency calculating percentiles and range spreads building market referencing models identifying gaps and recommending adjustments and forecasting cost impact of market moves. Candidates should explain the calculations used such as median and quartile calculation describe assumptions and limitations and show how to communicate recommendations to stakeholders.
Statistical Analysis and Interpretation
Demonstrate sound statistical methods for compensation data analysis and the ability to translate quantitative findings into business insight. Core skills include descriptive statistics and distributional analysis (percentiles, median, mean, standard deviation, measures of dispersion), outlier detection and handling, correlation assessment, basic and multivariable regression to control for confounders such as tenure or location, hypothesis testing with interpretation of p values and confidence intervals, evaluation of sample size and statistical power, and awareness of pitfalls such as confounding, multiple comparisons, and model assumption violations. Candidates should also explain how to choose appropriate tests for pay audits, validate models, and present statistical results clearly to nontechnical stakeholders.
Data Storytelling and Insight Communication
Skills for converting quantitative and qualitative analysis into a clear, persuasive narrative that guides stakeholders from findings to action. This includes leading with the headline insight, defining the business question, selecting the most relevant metrics and visual evidence, and structuring a concise story that explains what happened, why it happened, and what the recommended next steps are. Candidates should demonstrate tailoring of language and technical depth for diverse audiences from engineers to product managers to executives, summarizing trade offs and uncertainty in plain language, distinguishing correlation from causation, proposing follow up experiments or investigations, and producing concise executive summaries and status reports with an appropriate cadence. Interviewers evaluate the ability to persuade and align cross functional partners, answer questions about data validity and methodology, synthesize qualitative signals with quantitative results, and adapt presentation format and level of detail to the decision maker.
Insight Translation and Recommendations
The ability to move beyond reporting numbers to produce clear, actionable business recommendations and narratives. This includes summarizing the problem statement, approach, key findings, model or analysis performance, limitations, and recommended next steps framed as business actions. Candidates should demonstrate how insights map to business metrics and priorities, quantify potential impact and tradeoffs, propose experiments or interventions, and prioritize recommended actions. Effective communication techniques include concise storytelling, appropriate visualizations, translating technical metrics into business terms, anticipating stakeholder questions, and explicitly answering the questions so what and now what. Senior analysts connect root cause analysis to concrete proposals such as feature changes, pricing experiments, targeted support, or investment decisions, and explain risks, data assumptions, and implementation considerations.
Business Impact Measurement and Metrics
Selecting, measuring, and interpreting the business metrics and outcomes that demonstrate value and guide decisions. Topics include high level performance indicators such as revenue decompositions, lifetime value, churn and retention, average revenue per user, unit economics and cost per transaction, as well as operational indicators like throughput, quality and system reliability. Candidates should be able to choose leading versus lagging indicators for a given question, map operational KPIs to business outcomes, build hypotheses about drivers, recommend measurement changes and define evaluation windows. Measurement and attribution techniques covered include establishing baselines, experimental and quasi experimental designs such as A B tests, control groups, difference in differences and regression adjustments, sample size reasoning, and approaches to isolate confounding factors. Also included are quick back of the envelope estimation techniques for order of magnitude impact, converting technical metrics into business consequences, building dashboards and health metrics to monitor programs, communicating numeric results with confidence bounds, and turning measurement into clear stakeholder facing narratives and recommendations.
Data Interpretation & Dashboard Literacy
Practice interpreting data visualizations, trend lines, and metric dashboards. Develop ability to identify what's noteworthy (seasonality, anomalies, correlations) vs. normal variation. Think about causation vs. correlation. Practice explaining what a metric trend means in business terms and what actions it might suggest.
Data Analysis and Insight Generation
Ability to convert raw data into clear, evidence based business insights and prioritized recommendations. Candidates should demonstrate end to end analytical thinking including data cleaning and validation, exploratory analysis, summary statistics, distributions, aggregations, pivot tables, time series and trend analysis, segmentation and cohort analysis, anomaly detection, and interpretation of relationships between metrics. This topic covers hypothesis generation and validation, basic statistical testing, controlled experiments and split testing, sensitivity and robustness checks, and sense checking results against domain knowledge. It emphasizes connecting metrics to business outcomes, defining success criteria and measurement plans, synthesizing quantitative and qualitative evidence, and prioritizing recommendations based on impact feasibility risk and dependencies. Practical communication skills are assessed including charting dashboards crafting concise narratives and tailoring findings to non technical and technical stakeholders, along with documenting next steps experiments and how outcomes will be measured.
Visualization Selection and Effectiveness
Demonstrating the ability to choose appropriate chart types for different data patterns (trends over time, categorical comparisons, distributions, correlations). Creating visualizations that communicate clearly without ambiguity. Using color, formatting, and labels effectively to enhance understanding.