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.
Predictive Analytics & Quality Of Hire Measurement
Understanding how to measure quality-of-hire beyond just time-to-fill, including retention rates, performance ratings, internal promotion rates, and engagement scores of new hires. Using historical data to predict which candidate profiles are most successful.
Data Driven Problem Solving in HR Operations
Addresses using data and analytics to diagnose and solve human resources and people operations problems. Candidates should demonstrate hypothesis formulation, metric and experiment design, cohort and pipeline analysis, and translating analytics into operational actions. Typical examples include analyzing exit interviews and retention drivers, measuring onboarding ramp and time to productivity, identifying hiring pipeline bottlenecks, evaluating training program effectiveness, and designing experiments or multivariate analyses to test interventions. Senior level answers should include how to move beyond descriptive reporting to causal inference, experimentation, and measurable outcomes tied to business goals.
Human Resources and Recruitment Analytics
Human Resources and Recruitment Analytics covers applying data analysis, reporting, and measurement to talent acquisition, retention, and workforce planning. Candidates should be able to define and interpret common people metrics such as turnover and attrition rates, time to hire and time to fill, cost per hire, candidate funnel conversion rates, source effectiveness, diversity and inclusion measures, employee engagement and promotion rates. Core skills include extracting and preparing data from human resources information systems and other sources, exploratory and root cause analysis, designing and building operational dashboards and executive scorecards, creating analytical reports for hiring funnels and attrition deep dives, forecasting hiring needs, and measuring recruitment effectiveness and return on investment. It also includes designing recruitment reporting frameworks, running experiments or interventions to improve retention and hiring outcomes, constructing business cases for recruitment investments, and communicating findings and recommendations in clear business terms. Candidates should be mindful of privacy, compliance, and bias mitigation when working with people data and familiar with data visualization and dashboarding workflows and stakeholder-driven reporting practices.
Data Driven Recommendations and Impact
Covers the end to end practice of using quantitative and qualitative evidence to identify opportunities, form actionable recommendations, and measure business impact. Topics include problem framing, identifying and instrumenting relevant metrics and key performance indicators, measurement design and diagnostics, experiment design such as A B tests and pilots, and basic causal inference considerations including distinguishing correlation from causation and handling limited or noisy data. Candidates should be able to translate analysis into clear recommendations by quantifying expected impacts and costs, stating key assumptions, presenting trade offs between alternatives, defining success criteria and timelines, and proposing decision rules and go no go criteria. This also covers risk identification and mitigation plans, prioritization frameworks that weigh impact effort and strategic alignment, building dashboards and visualizations to surface signals across HR sales operations and product, communicating concise executive level recommendations with data backed rationale, and designing follow up monitoring to measure adoption and downstream outcomes and iterate on the solution.