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.
Analytics and Dashboarding
Designing, building, and enabling dashboards and spreadsheet based analysis to turn data into actionable insights for different stakeholder audiences. Candidates should be able to define and prioritize key performance indicators and metrics for roles such as sales, marketing, finance, and executives; apply dashboard design principles that present complex data clearly; and enable self service analytics through reusable data models, standardized metrics, documentation, and user training. Practical spreadsheet skills are included: advanced formulas, pivot tables, lookup functions, data cleaning, filtering, charting, sensitivity and what if analysis, and performance optimization. Candidates should also speak to tools and platforms used such as Excel, Google Sheets, business intelligence platforms, visualization tools, and analytics platforms; consider refresh cadence, data validation and governance, interactivity and drill down patterns, and trade offs between standardized reporting and bespoke custom views.
Business to Data Translation
Covers the practice of turning a business question into a concrete data analysis or query. This includes defining precise metrics and filters, selecting the right aggregation level, mapping the question to available tables and fields, drafting example queries or pseudocode, validating results and counts, identifying data quality or instrumentation gaps, and communicating limitations to stakeholders. Interviewers evaluate the candidate's ability to reason about metric definitions, cohort construction, time windows, and trade offs between freshness and completeness.
Analysis to Recommendation and Decision Framing
Ability to move from analysis to a concise, justified recommendation and a pragmatic plan for decision and implementation. Candidates should lead with a clear recommendation or conditional decision, support it with evidence and trade offs, quantify expected business impact, estimate effort and time horizon, and state assumptions and limitations. The skill set includes proposing prioritized action plans and alternative options, anticipating objections, defining monitoring and rollback strategies, translating technical remediation or risk into business terms and measurable success metrics, and tailoring recommendations to stakeholder needs and constraints.
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 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.
Data Analysis and Requirements Translation
Focuses on translating ambiguous business questions into concrete data analysis plans. Candidates should identify the data points required, define metrics and key performance indicators, state assumptions to validate, design the analysis steps and queries, and explain how analysis results map back to business decisions. This includes data quality considerations, required instrumentation, and how analytical findings influence product requirements or architectural choices.