Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Dashboard and Data Visualization Design
Principles and practices for designing, prototyping, and implementing visual artifacts and interactive dashboards that surface insights and support decision making. Topics include information architecture and layout, chart and visual encoding selection for comparisons trends distributions and relationships, annotation and labeling, effective use of color and white space, and trade offs between overview and detail. The topic covers interactive patterns such as filters drill downs tooltips and bookmarks and decision frameworks for when interactivity adds user value versus complexity. It also encompasses translating analytic questions into metrics grouping related measures, wireframing and prototyping, performance and data latency considerations for large data sets, accessibility and mobile responsiveness, data integrity and maintenance, and how statistical concepts such as statistical significance confidence intervals and effect sizes influence visualization choices.
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 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.
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.
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.
Data Informed HR Thinking
Practice scenarios that involve thinking through HR metrics or data: analyzing turnover in a team, understanding the impact of a policy change, or identifying trends. Learn to: (1) Ask what data is available, (2) Understand what the data shows vs. what it doesn't, (3) Consider context and potential causes, (4) Propose data-informed actions. Even at entry level, show that you think about cause and effect, not just isolated incidents. Demonstrate curiosity about metrics and their business meaning.
Program Evaluation and Measurement
Assessing whether learning, people, and other organizational programs achieve their objectives and deliver measurable value. This includes defining success criteria and baseline metrics before implementation, selecting quantitative and qualitative measures during and after delivery, and understanding different measurement levels such as reaction, learning, behavior, and results as described in the Kirkpatrick model. Candidates should be able to design evaluation plans that include completion and engagement metrics, knowledge and skill assessments, behavior or application measures, retention and performance indicators, and business outcomes. The description should cover leading and lagging indicators, approaches to isolating program impact from confounding factors, simple experimental or quasi experimental designs when feasible, pragmatic trade offs between ideal and practical measurement, data collection methods and tools, calculating and communicating return on investment both financial and non financial, and tailoring reporting to stakeholders. Examples might include measuring onboarding effects on time to productivity, mentorship impact on retention, or communications effectiveness on benefits adoption. For junior roles, demonstrate familiarity with how to think about measurement choices and limitations; for senior roles, include designing robust evaluation frameworks and translating findings into business recommendations.
Human Resources Analytics and Technology
Experience working with human resources technology ecosystems and analytics tools for workforce and people analytics. Topics include using human resources management systems and applicant tracking systems that expose reporting capabilities extracting and transforming HR data in spreadsheets and databases building dashboards and reports in business intelligence tools such as Tableau Power BI and Looker and writing basic database queries for ad hoc analysis. Candidates should be able to discuss data models common HR metrics such as headcount turnover time to hire diversity and inclusion metrics compensation analysis and workforce planning; explain how data is structured stored and accessed in HR systems and describe privacy security and compliance considerations when working with employee data.
Employee Experience Metrics and Measurement
Ability to identify and track metrics that measure employee experience: new-hire satisfaction/retention, engagement scores, employee NPS, survey participation rates, internal transfer rates, voluntary turnover, time-to-productivity, etc. At junior level, demonstrate comfort analyzing these metrics, identifying trends, and recommending actions based on data.