Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
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.
Measurement Design and Analysis
Practical measurement design and analytic techniques for producing reliable metric signals and proving impact. Includes instrumentation and tracking plans, experiment selection and validation, attribution modeling and its limitations, sample size and statistical considerations, identifying confounding variables, and reasoning about correlation versus causation. Also covers tradeoffs in data collection and data quality checks, cohort and segmentation design, baselining and threshold setting, designing dashboards and monitoring cadence, and connecting engineering and telemetry data to business outcomes. Candidates should be able to write clear measurement plans and success criteria, describe experiment and validation approaches, and explain how to operationalize results through reporting and iteration.
Engineering and Business Outcomes
How engineering work and technical decisions translate into measurable business outcomes and how to demonstrate that linkage. Topics include mapping architecture choices, reliability, performance improvements and developer productivity initiatives to business metrics such as revenue, customer engagement, time to market, cost reduction and customer satisfaction. Candidates should be able to identify engineering metrics to track including latency, availability, error and incident rates, cycle time and deployment frequency, explain instrumentation strategies to capture signals, design measurement plans and experiments to establish causal impact, and attribute observed changes to specific engineering efforts. This topic also covers communicating technical tradeoffs and impact to nontechnical stakeholders, choosing appropriate granularity for measurement, and describing concrete initiatives with their measurement approach and quantified business impact.
Metrics Analysis and Data Driven Problem Solving
Skills for using quantitative metrics to diagnose and solve business, product, or operational problems across functions. Candidates should be able to identify the key performance indicators relevant to their domain (for example: conversion rate, retention, revenue per user, pipeline velocity, response time, or customer satisfaction), detect anomalies and trends in metrics, formulate and prioritize hypotheses about root causes, design experiments and controlled tests (such as A/B tests) to validate hypotheses, perform cohort and time series analysis, evaluate statistical significance versus practical business impact, and implement and monitor data backed solutions. This also includes instrumentation and data collection best practices, dashboarding and visualization to surface insights, trade off analysis when balancing multiple competing metrics, and communicating findings and recommended changes to cross functional stakeholders.
Structured Query Language Fundamentals and Aggregation
This topic covers core Structured Query Language fundamentals for analytical querying and reporting. Candidates should be able to write correct, readable, and maintainable SELECT queries with filtering using WHERE, sorting with ORDER BY, grouping with GROUP BY, and group filtering with HAVING. They should apply aggregate functions such as COUNT, COUNT DISTINCT, SUM, AVG, MIN, and MAX and understand how NULL values affect results, how empty result sets behave, and when to use different counting approaches. The scope includes date and time filtering, basic cohort segmentation, and common time based comparisons used to compute metrics such as daily active users, average revenue per user, and period over period comparisons. Candidates are expected to use basic joins and join predicates including inner joins and left joins, write simple subqueries and conditional expressions, and perform common data transformation and cleansing patterns to prepare data for analysis. Finally, this topic assesses query readability and maintainability practices such as aliasing and formatting, plus awareness of elementary performance considerations including index usage and avoiding unnecessary full table scans for entry to mid level analytical tasks.
Data Driven Problem Solving and Recommendations
Use data to define business problems, form and test hypotheses, identify root causes, and produce clear, prioritized, evidence based recommendations. Candidates should be able to translate a business question into measurable metrics, choose appropriate analyses, segment and compare cohorts, validate assumptions, quantify expected impact and implementation effort, and surface limitations or data quality issues. Good answers explain the analysis steps, any assumptions made, how results would be validated, and the communication approach to ensure stakeholders can act on the recommendation.
Analytical Problem Solving and Hypothesis Testing
Assesses the candidate's ability to convert an ambiguous business problem or signal into structured hypotheses, design analyses or experiments, and draw defensible conclusions. Expect discussion of problem framing, prioritizing hypotheses, selecting data sources, defining cohorts and metrics, designing queries or tests, validating assumptions, controlling for confounders, and communicating actionable recommendations. Core skills include critical thinking, data exploration, statistical reasoning, and translating insights into measurable action plans.
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.