Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
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.
SQL for Data Analysis
Using SQL as a tool for data analysis and reporting. Focuses on writing queries to extract metrics, perform aggregations, join disparate data sources, use subqueries and window functions for trends and rankings, and prepare data for dashboards and reports. Includes best practices for reproducible analytical queries, handling time series and date arithmetic, basic query optimization considerations for analytic workloads, and when to use SQL versus built in reporting tools in analytics platforms.
Data Driven Decision Making
Using metrics and analytics to inform operational and strategic decisions. Topics include defining and interpreting operational measures such as throughput cycle time error rates resource utilization cost per unit quality measures and on time delivery, as well as growth and lifecycle metrics across acquisition activation retention and revenue. Emphasis is on building audience segmented dashboards and reports presenting insights to influence stakeholders diagnosing problems through variance analysis and performance analytics identifying bottlenecks measuring campaign effectiveness and guiding resource allocation and investment decisions. Also covers how metric expectations change with seniority and how to shape organizational metric strategy and scorecards to drive accountability.
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.
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.
Metrics Selection and Dashboard Storytelling
Focuses on selecting metrics and designing dashboards and reports that directly support stakeholder decision making. Candidates should be able to identify distinct audiences and the specific decisions each audience must make, choose actionable metrics rather than vanity metrics, and balance leading indicators with lagging indicators as well as strategic metrics with operational metrics. This topic covers defining key performance indicators and targets and justifying each metric by the decision it enables, setting data freshness requirements and update cadence, and ensuring instrumentation and data quality to make metrics reliable. It includes dashboard architecture and visual narrative design such as layering from high level summaries to detailed drill down, tailoring views for executives, managers, and operational teams, selecting appropriate visualizations and annotations to guide interpretation, and enabling root cause analysis. Reporting practices are covered, including formatting, distribution channels, and alerting. Governance and metric definition topics include creating a single source of truth, assigning ownership, documenting definitions, and change control. Candidates must also recognize metric interactions and common pitfalls that can make metrics misleading such as aggregation bias, sampling issues, correlation versus causation, and perverse incentives, and propose mitigations. Interview questions typically ask candidates to design metric sets and dashboards for hypothetical scenarios, explain why metrics were chosen based on decisions they support, and describe cadence, distribution, drilling, and governance approaches.
Correlation vs. Causation and Confounding Variables
Recognize that correlation (statistical relationship between variables) doesn't imply causation (direct cause-and-effect relationship). Identify confounding variables that might explain an observed correlation. For example, summer ice cream sales and crime rates both increase but neither causes the other—warm weather is the confounder. Practice identifying lurking variables in business scenarios.
Audience Segmentation and Cohorts
Covers methods for dividing users or consumers into meaningful segments and analyzing their behavior over time using cohort analysis. Candidates should be able to choose segmentation dimensions such as demographics, acquisition channel, product usage, geography, device, or behavioral attributes, and justify those choices for a given business question. They should know how to design cohort analyses to measure retention, churn, lifetime value, and conversion funnels, and how to avoid common pitfalls such as Simpson's Paradox and survivorship bias. This topic also includes deriving behavioral insights to inform personalization, content and product strategy, marketing targeting, and persona development, as well as identifying underserved or high value segments. Expect discussion of relevant metrics, data requirements and quality considerations, approaches to visualization and interpretation, and typical tools and techniques used in analytics and experimentation to validate segment driven hypotheses.
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.