McKinsey Data Analyst Interview: Top Questions Shared by Candidates (2026)

The McKinsey data analyst role sits at the center of how modern consulting decisions are made. As McKinsey & Company continues to invest heavily in advanced analytics, digital transformation, and specialized teams like QuantumBlack, data analysts play a critical role in turning messy client data into clear, executive-ready insights. From banking and insurance to health systems and growth marketing, analysts support high-stakes client engagements where accuracy, structure, and judgment matter just as much as technical skill.

The McKinsey data analyst interview reflects this responsibility. You are evaluated on far more than SQL correctness or statistical knowledge. Interviewers look for structured problem solving, strong business intuition, and the ability to communicate insights that influence senior stakeholders. This guide outlines each stage of the McKinsey data analyst interview, highlights the most common data analyst specific interview questions, and shares proven strategies to help you stand out and prepare effectively with Interview Query.

The McKinsey data analyst interview process is structured to evaluate how effectively you use data to support real consulting decisions. Across stages, interviewers assess analytical rigor, SQL fluency, structured thinking, and your ability to communicate insights clearly under ambiguity. While details vary slightly by office and analytics group, the overall process follows a consistent sequence and typically takes three to five weeks from initial screen to final decision.

Application and Resume Screen

The process begins with a resume review by McKinsey recruiters and analytics hiring managers. At this stage, they look for evidence of strong analytical foundations, comfort working with imperfect data, and clear impact from past work. Experience using SQL to support business decisions, performing exploratory analysis, or contributing to strategy or operations projects is especially valued.

Resumes that focus on outcomes rather than tools tend to stand out. McKinsey is less concerned with which platforms you used and more focused on how your analysis shaped decisions.

Tip: Frame your experience around problem statements and results, not just tasks. This demonstrates structured thinking and client impact, two core skills McKinsey evaluates early.

Online Assessment or Analytics Screening

Some McKinsey data analyst roles include an online assessment or structured analytics screening. This stage evaluates quantitative reasoning, basic data interpretation, and comfort working through time-bound analytical problems. You may be asked to interpret charts, reason through numerical scenarios, or evaluate trade-offs using limited information.

The goal is not speed alone, but clarity of logic and consistency in your reasoning.

Tip: Practice articulating your logic clearly even when answers seem straightforward. McKinsey values candidates who show discipline in how they reason, not just correct outcomes.

Technical Experience Interview

The technical experience interview focuses on how you approach real analytics work. You may be asked to write SQL queries, interpret outputs, assess data quality issues, or walk through a past project where your analysis influenced a decision. Interviewers care deeply about how you structure problems, validate assumptions, and explain results.

This round often blends technical execution with business interpretation.

Tip: Always connect your analysis back to a decision or recommendation. This shows consulting judgment, not just technical competence.

Case Interviews for Data Analyst Candidates

Data analyst candidates still complete case-style interviews, but with a stronger quantitative and data interpretation focus. You may analyze exhibits, reason through market sizing, or assess the implications of numerical trends for a client strategy. Interviewers evaluate structure, clarity, and how you prioritize information under uncertainty.

You are not expected to memorize frameworks, but you are expected to think logically and communicate clearly.

Tip: State your assumptions out loud and explain why they are reasonable. This signals transparency and strong problem framing, both highly valued at McKinsey.

Final Onsite Interviews

The final onsite stage is the most comprehensive part of the McKinsey data analyst interview process. It typically includes three to four interviews, each lasting around 45 to 60 minutes, and is designed to assess readiness for real case team work.

1. Advanced data analysis interview: You will work through a deeper analytical problem that may involve interpreting multiple data cuts, identifying drivers, or pressure-testing conclusions. Interviewers assess how well you balance accuracy with speed and how clearly you explain your thinking.

   Tip: Summarize insights as if presenting to a partner. This demonstrates executive-level communication and decision focus.

2. Quantitative case interview: This round blends traditional case problem solving with analytics. You may size an opportunity, analyze trends, or reason through trade-offs using partial data. Structure and clarity matter more than perfect math.

   Tip: Focus on framing the problem correctly before calculating. Strong structure signals consulting maturity.

3. Stakeholder communication interview: Interviewers evaluate how you explain data to non-technical audiences and respond to pushback. You may be asked how you would defend assumptions or adjust analysis based on client concerns.

   Tip: Practice concise explanations without technical jargon. This shows influence and client-facing readiness.

4. Behavioral and collaboration interview: This interview assesses teamwork, learning mindset, and ownership. Expect questions about feedback, ambiguity, and working through challenges on a team.

   Tip: Emphasize reflection and growth, not just success. McKinsey values coachability and long-term potential.

Hiring decision and offer

After the onsite, interviewers submit independent feedback that is reviewed collectively. Decisions are based on performance across analytics, problem solving, communication, and cultural alignment. Successful candidates may be matched to a specific analytics or capability group before receiving an offer.

Tip: If you have strong preferences for industry exposure or analytics focus, communicate them clearly. McKinsey often considers fit when finalizing placement.

Want to build up your data analysis interview skills? Practicing real hands-on problems on the Interview Query Dashboard and start getting interview ready today.

The McKinsey data analyst interview questions are designed to test how well you apply analytics in real consulting scenarios. Questions span SQL and data analysis, quantitative case reasoning, technical experience discussions, and behavioral interviews rooted in McKinsey’s Personal Experience Interview format. Across all categories, interviewers look for structured thinking, clean logic, and the ability to connect data to decisions clients actually care about at McKinsey & Company.

SQL and Analytics Interview Questions

These questions evaluate how you use SQL as a thinking tool rather than a coding exercise. McKinsey data analysts are expected to structure ambiguous problems, validate messy client data, and extract insights that directly inform case direction. Interviewers use these questions to see whether you can translate business questions into clean analytical logic and explain results in a way that consultants and partners can act on.

1. How would you write a SQL query to identify the top three drivers of revenue decline across regions?

   This question tests whether you can decompose a vague business problem into measurable components. At McKinsey, revenue decline analysis is a common early case task, and interviewers want to see how you decide which dimensions matter such as region, product, channel, or customer segment. To answer, you would first define revenue consistently, join transactional data to region metadata, aggregate revenue by driver over time, calculate deltas, and rank contributors to the decline rather than just reporting totals.

   Tip: Before writing SQL, explain how you chose the drivers you analyzed. This shows structured problem framing and prioritization, which partners rely on when time is limited.

2. Write a SQL query to determine whether each project is over budget or within budget by comparing its prorated employee salary costs to its allocated budget.

   This question evaluates your ability to translate real operational constraints into SQL logic. McKinsey frequently works with project-based cost data, where analysts must allocate shared resources accurately. To solve this, you would calculate prorated salary costs based on time or allocation percentage, aggregate costs at the project level, and compare them to the budget field to flag over- or under-budget projects.

   Tip: Call out assumptions explicitly, such as how salaries are prorated. This signals financial reasoning and credibility, which are critical when analysis informs cost decisions.

3. How would you identify outliers in customer spend using SQL?

   This question tests whether you understand distributions and business context, not just math. At McKinsey, outliers can signal data quality issues, fraud, or high-value opportunities. A strong answer explains choosing an approach such as percentile thresholds or standard deviation bands, calculating those metrics in SQL, and then filtering customers who fall outside expected ranges while validating whether those values are meaningful or errors.

   Tip: Always explain what you would do after finding outliers. This shows judgment and avoids the common analyst mistake of stopping at detection instead of interpretation.

4. Write a SQL query to count how many users who signed up in January 2020 had over $100 in total successful sent and received payments within their first 30 days.

   This question assesses cohort analysis and time-based filtering, both common in McKinsey growth and performance cases. To answer, you would filter users by signup date, join to transactions, restrict to successful payments within a 30-day window from signup, aggregate sent and received amounts, and count users above the threshold. Interviewers care about whether you align time windows correctly and avoid double counting.

   Tip: Clearly state how you define the 30-day window. Precision with time logic signals analytical rigor and prevents misleading conclusions.

   

   Head to the Interview Query dashboard to practice McKinsey-style data analyst interview questions. You can work through SQL, analytics, case-based, and behavioral questions with built-in code execution and AI-guided feedback, making it easier to prepare for the analytical rigor, structured thinking, and client-focused judgment McKinsey interviews emphasize.

5. Given a users table, write a query to get the cumulative number of new users added by the day, with the total reset every month. 

   This question tests window functions and metric construction. McKinsey analysts often track cumulative metrics to show momentum or progress to clients. A strong solution groups users by signup date, orders by day, and uses a window function partitioned by month to compute cumulative counts that reset monthly. The key is explaining why this metric is useful and how it would be interpreted in a client discussion.

   Tip: Tie the metric to a decision, such as evaluating campaign effectiveness. This demonstrates that you think beyond SQL output to client impact.

Watch Next: Top 5 Insider Interview Questions Data Analysts Must Master Before Any Interview!

In this video, Jay Feng, an experienced data scientist and cofounder of Interview Query, walks through five data analyst interview questions that mirror how McKinsey evaluates analytical thinking. The discussion focuses on breaking down ambiguous prompts, selecting appropriate analytical methods, implementing clear solutions, and, most importantly, tying results back to the business or client decision at hand. It is a strong example of how to balance technical rigor with structured reasoning and executive-ready communication, which are core expectations for McKinsey data analysts.

Quantitative Case and Data Interpretation Interview Questions

These questions test how well you combine case-style structuring with numerical reasoning. At McKinsey, data analysts are expected to interpret incomplete information, pressure-test numbers, and translate quantitative findings into implications a case team can act on. Interviewers are less interested in perfect math and more focused on how you frame the problem, choose the right cuts, and communicate trade-offs clearly.

1. A client’s profits dropped despite flat revenue. How would you analyze this using data?

   This question tests whether you can decompose a business problem logically. At McKinsey, profit declines often hide in cost structure, mix shifts, or operational inefficiencies. A strong answer starts by breaking profit into revenue minus costs, then further decomposes costs into fixed versus variable, volume versus mix, and unit economics. You would use data to isolate which component changed and quantify its impact before jumping to conclusions.

   Tip: Explicitly state the order of your analysis before diving into numbers. This shows structured problem solving, which is critical in fast-moving case environments.

2. How would you size the impact of a five percent price increase using limited data?

   This question evaluates comfort with assumptions and back-of-the-envelope math. McKinsey often works with partial data early in cases, and analysts must still provide directional guidance. A strong answer explains estimating baseline revenue, applying the price increase, and adjusting for demand elasticity or churn risk. You should discuss scenarios rather than a single number to reflect uncertainty and inform decision-making.

   Tip: Share a base case and a downside case. This demonstrates judgment under uncertainty and helps partners assess risk, not just upside.

3. How would you interpret differences between unit sales and revenue contribution across pricing tiers, and which tier should be prioritized for future growth?

   This question tests your ability to reason about mix and margin. At McKinsey, analysts frequently see products with high volume but low revenue contribution or vice versa. A good answer explains analyzing average price, margins, and growth trends by tier, then evaluating trade-offs between scaling volume and protecting profitability. The recommendation should tie directly to the client’s strategic objective.

   Tip: Avoid defaulting to the highest revenue tier. Explain why the chosen tier best aligns with the client’s growth or margin goals.

   

   Head to the Interview Query dashboard to practice McKinsey-style data analyst interview questions. You can work through SQL, analytics, case-based, and behavioral questions with built-in code execution and AI-guided feedback, making it easier to prepare for the analytical rigor, structured thinking, and client-focused judgment McKinsey interviews emphasize.

4. If January subscribers have a 10% first-cycle churn rate, new signups are uniform monthly, and churn decreases by 20% month over month, what is the expected overall churn rate in March for all customers acquired since January 1st?

   This question tests whether you can reason through layered assumptions without getting lost in math. McKinsey interviewers look for clarity in cohort logic rather than exact arithmetic. A strong answer walks through how churn evolves by cohort over time, applies the decay correctly, and aggregates across cohorts to estimate March churn, explaining each step clearly.

   Tip: Talk through the logic before calculating. Clear cohort reasoning is often more important than numerical precision.

5. How would you analyze and visualize churn behavior across monthly and annual subscription plans to give executives a clear view of overall subscription performance?

   This question evaluates synthesis and executive communication. At McKinsey, data analysts must present churn in a way that highlights risk and opportunity. A strong answer explains segmenting by plan type, normalizing churn metrics for different billing cycles, and choosing visuals that compare retention curves over time. The goal is clarity, not analytical complexity.

   Tip: Design visuals to answer one decision-focused question per chart. This shows executive-level communication and avoids overloading stakeholders.

Looking for hands-on problem-solving? Test your skills with real-world challenges from top companies. Ideal for sharpening your thinking before interviews and showcasing your problem solving ability.

Technical Experience Interview Questions

These questions evaluate how you apply analytics in real-world settings, especially when data is messy, stakeholders have competing priorities, and decisions must be made quickly. At McKinsey, interviewers use technical experience questions to understand your judgment, problem framing, and ability to translate analysis into action, not just your technical knowledge.

1. How would you integrate, clean, and analyze data from transactions, user behavior, and fraud logs to generate insights that improve overall system performance?

   This question tests how you handle multi-source data integration, a common challenge in McKinsey client work. A strong answer explains how you would align definitions across sources, validate joins, handle missing or conflicting records, and sequence analysis to avoid compounding errors. You should then describe how insights would be tied to performance levers such as conversion, risk reduction, or operational efficiency.

   Tip: Explain how you would prioritize data sources when timelines are tight. This demonstrates judgment and the ability to move forward without waiting for perfect data.

2. Describe an analytics experiment that you designed. How were you able to measure success?

   This question evaluates whether you understand experimentation beyond basic metrics. At McKinsey, experiments are used to de-risk decisions, not just measure lift. A strong answer walks through hypothesis definition, control and treatment setup, success metrics, and guardrails. You should also explain how results influenced a decision rather than ending with statistical significance alone.

   Tip: Emphasize how the experiment informed a decision. McKinsey values experiments as learning tools, not just performance scorecards.

3. What is the formula to calculate average customer lifetime value for a SaaS product given monthly price, churn rate, and average customer lifespan?

   This question tests whether you understand core business metrics and their assumptions. A good answer explains the formula conceptually, links churn to expected lifespan, and highlights when the simplified calculation breaks down. McKinsey interviewers care more about whether you understand what drives lifetime value than whether you recall a formula from memory.

   Tip: Call out the assumptions behind the formula. This signals financial intuition and prevents overconfidence in simplified models.

4. How would you analyze transaction, pricing, discount, and marketing data to pinpoint the drivers behind a year-long revenue decline in an e-commerce business?

   This question evaluates structured root cause analysis. At McKinsey, analysts are expected to break revenue into volume, price, mix, and customer effects. A strong answer explains how you would sequence analysis, isolate drivers using data cuts, and quantify each factor’s contribution before recommending actions.

   Tip: Describe how you would rule out false signals early. This shows analytical discipline and avoids chasing noise.

   

   Head to the Interview Query dashboard to practice McKinsey-style data analyst interview questions. You can work through SQL, analytics, case-based, and behavioral questions with built-in code execution and AI-guided feedback, making it easier to prepare for the analytical rigor, structured thinking, and client-focused judgment McKinsey interviews emphasize.

5. How would you measure and optimize the performance of a $100K 30-day digital marketing campaign across paid social and search to drive new subscription signups?

   This question tests your ability to connect analytics to resource allocation. A strong answer outlines defining success metrics, attributing conversions across channels, comparing marginal returns, and recommending budget shifts. McKinsey interviewers want to see that you think in terms of trade-offs and opportunity cost, not just reporting results.

   Tip: Frame optimization as an ongoing decision process, not a one-time analysis. This demonstrates strategic thinking and adaptability.

Business Judgment and Behavioral Interview Questions

These questions evaluate how you translate analysis into decisions, influence senior stakeholders, and operate under ambiguity. At McKinsey & Company, data analysts are expected to pair analytical rigor with sound judgment, concise communication, and ownership. Interviewers listen for how you adapt your message, defend insights with evidence, and learn from setbacks in client-facing environments.

1. How would you explain a complex analysis to a non technical partner?

   This question assesses your ability to distill complexity into clarity. McKinsey partners and clients rely on analysts to surface the “so what” quickly, often minutes before a decision is made. Interviewers want to see how you structure explanations, choose the right level of detail, and keep the conversation focused on decisions rather than methodology.

   Sample answer: On a previous engagement, I had to brief a senior partner on a multi-step pricing analysis ahead of a client meeting. I started with the recommendation and its expected impact, then used one chart to show the primary driver. When questions came up, I referenced assumptions and data sources without diving into technical detail. The partner used the takeaway directly in the client discussion, which helped align the team on the pricing approach.

   Tip: Lead with the conclusion and decision impact. This demonstrates executive communication and shows you understand how senior stakeholders consume analytics.

2. What do you do when analysis contradicts a senior stakeholder’s intuition?

   This question evaluates how you handle tension between data and experience. At McKinsey, analysts are expected to challenge constructively while maintaining trust. Interviewers listen for how you present evidence, acknowledge intuition, and guide stakeholders toward validation rather than confrontation.

   Sample answer: On one case, a senior stakeholder believed demand was driven primarily by pricing, while my analysis pointed to channel mix as the main driver. I acknowledged their perspective, walked through a focused data cut that isolated the effect, and proposed a small follow-up test. The results confirmed the finding and helped shift the team’s strategy without creating friction.

   Tip: Pair evidence with respect and a validation path. This shows influence, judgment, and client-ready maturity.

3. How would you handle client pushback on a proposed analytics dashboard, negotiate alternative solutions, and ensure the disagreement leads to a constructive and successful outcome?

   This question tests collaboration and adaptability in real client situations. McKinsey analysts must design outputs that fit how clients actually make decisions, not just what looks analytically impressive. Interviewers want to see how you listen, adjust, and still protect analytical integrity.

   Sample answer: A client pushed back on a dashboard they felt was too complex for weekly decision making. I met with their team to understand their operating rhythm, reduced the view to three core KPIs, and added drill-downs for deeper analysis. Adoption increased within weeks, and the dashboard became part of their regular leadership reviews.

   Tip: Optimize for the decision workflow, not the dashboard itself. This signals client empathy and outcome-driven thinking.

   

   Head to the Interview Query dashboard to practice McKinsey-style data analyst interview questions. You can work through SQL, analytics, case-based, and behavioral questions with built-in code execution and AI-guided feedback, making it easier to prepare for the analytical rigor, structured thinking, and client-focused judgment McKinsey interviews emphasize.

4. Tell me about a time you took ownership of a difficult problem.

   This question explores how you behave when responsibility is ambiguous and stakes are high. McKinsey values analysts who step in, make reasonable assumptions, and keep momentum even when information is incomplete. Interviewers look for ownership demonstrated through decisions, not titles.

   Sample answer: During a tight deadline, I owned an analysis with incomplete client data. I defined clear assumptions, ran sensitivity checks, and delivered a directional recommendation on time. The case team used it to steer discussions while we refined inputs later, avoiding delays and keeping the engagement on track.

   Tip: Emphasize decisions made under constraints. Ownership at McKinsey means moving the work forward responsibly, not waiting for perfect conditions.

5. Describe a failure and what you learned from it.

   This question assesses self-awareness and growth mindset. McKinsey interviewers are less concerned with the failure itself and more interested in whether you can extract lessons and change how you operate going forward.

   Sample answer: Early in my career, I overbuilt an analysis that missed the client’s core question. After feedback, I reframed my approach to start with the decision first, simplified outputs, and aligned visuals to actions. My subsequent analyses were faster, clearer, and better received by stakeholders.

   Tip: Focus on how your behavior changed after the failure. This demonstrates learning velocity, which McKinsey values highly.

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A McKinsey data analyst supports case teams by transforming complex, often imperfect client data into clear insights that guide strategic decisions. The role blends rigorous analytics with consulting judgment. You are expected to move quickly from raw data to structured findings, pressure-test assumptions, and help teams quantify tradeoffs for senior clients. Data analysts work across industries such as banking, insurance, health systems, and growth marketing, often under tight timelines where clarity and credibility matter as much as technical depth.

Tip: At McKinsey, strong data analysts show judgment, not just technical speed. In interviews, explain why you chose a specific metric, cut, or assumption and how it would influence a partner-level decision. This signals business intuition and client readiness, which matter as much as clean SQL.

Preparing for a McKinsey data analyst interview requires a different mindset than preparing for a purely technical analytics role. You are preparing to support real client decisions where time is limited, data is imperfect, and clarity matters more than complexity. Success depends on combining strong analytical fundamentals with consulting judgment, structured communication, and an understanding of how data supports strategy at McKinsey & Company.

Below is a focused preparation plan to help you prepare efficiently and deliberately.

• Build comfort working with imperfect and incomplete data: McKinsey analysts rarely receive clean, production-ready datasets. Practice making progress with missing fields, inconsistent definitions, and partial coverage. Focus on how you validate assumptions, test sensitivity, and communicate uncertainty without stalling decisions.

  Tip: Be ready to explain how you would move forward with eighty percent confidence when waiting for perfect data would delay a client decision. This demonstrates judgment under real consulting constraints.

• Practice structuring analysis before touching SQL: Interviewers care deeply about how you frame a problem. Before writing queries, outline the logic, metrics, and cuts you would use. This mirrors how McKinsey teams align on analysis plans before execution.

  Tip: Start every practice problem by stating your hypothesis and success metric out loud. This shows structured thinking and reduces rework, both critical consulting skills.

• Strengthen your ability to synthesize insights quickly: McKinsey values analysts who can distill complex findings into clear takeaways. Practice summarizing analyses in two or three sentences, focusing on what changed, why it matters, and what decision it supports.

  Tip: Train yourself to lead with the answer, then back it up. This signals executive-ready communication and client impact awareness.

• Prepare polished stories from past analytics work: Review your experience and identify two or three projects that highlight ambiguity, stakeholder tension, or trade-offs. Be ready to explain not just what you did, but why you chose one approach over another and what you would change in hindsight.

  Tip: Emphasize decision points, not tools. McKinsey interviewers listen for judgment and learning, not technical bravado.

• Simulate realistic interview pacing and pressure: McKinsey interviews move quickly and test composure. Practice back-to-back sessions that include one analytics problem, one case-style discussion, and one behavioral interview. Use realistic time limits and speak your reasoning clearly throughout.

  Use Interview Query’s Mock Interviews to rehearse McKinsey-style scenarios with expert feedback, or refine your approach through Coaching sessions for targeted guidance.

  Tip: After each mock, note where your explanations became unclear or over-detailed. Sharpening those moments often produces the biggest performance gains.

Struggling with take-home assignments? Get structured practice with Interview Query’s Take-Home Test Prep and learn how to ace real case studies.

How long does the McKinsey data analyst interview process take?

Most candidates complete the process within three to five weeks. Timelines vary by office, interviewer availability, and whether multiple analytics teams are reviewing your profile. Recruiters typically share next steps after each round to keep expectations clear.

Do McKinsey data analysts have to do traditional case interviews?

Yes, but the cases are more quantitatively driven than generalist consulting cases. You are evaluated on structured thinking, data interpretation, and numerical reasoning rather than polished business frameworks or industry knowledge.

How technical are McKinsey’s SQL and analytics interviews?

The technical bar focuses on clarity and correctness rather than obscure SQL tricks. Expect joins, aggregations, window functions, and validation logic tied to real business questions. Explaining your reasoning matters as much as writing the query.

Is prior consulting experience required for this role?

No. McKinsey hires data analysts from industry, analytics teams, and research backgrounds. What matters most is your ability to structure problems, communicate insights clearly, and apply data to decision making in ambiguous settings.

How important is business context compared to technical accuracy?

Both matter, but business context often differentiates strong candidates. Interviewers expect you to explain what your results mean for a client and how they would influence a decision, not just whether the numbers are correct.

What tools should I be familiar with before interviewing?

Strong SQL is essential, along with comfort interpreting data in spreadsheets or basic scripting environments. McKinsey is tool agnostic, so they care more about analytical logic than specific platforms or languages.

How does McKinsey evaluate behavioral fit for data analysts?

Behavioral interviews follow the Personal Experience Interview format and focus on ownership, learning mindset, and collaboration. Interviewers look for evidence that you can handle feedback, ambiguity, and pressure on client teams.

Can data analysts transition into other roles at McKinsey?

Yes. Many data analysts move into consulting, analytics leadership, or specialized expert tracks over time. Performance, problem solving strength, and client impact play a larger role than your initial title at McKinsey & Company.

Preparing for the McKinsey data analyst interview means developing strong analytical fundamentals, structured problem solving, and the ability to translate data into clear, client-ready insights. By understanding McKinsey’s interview structure, practicing real-world SQL, refining your quantitative case thinking, and sharpening how you communicate trade-offs and assumptions, you can approach each stage with confidence. For targeted preparation, explore the full Interview Query question bank, practice with the AI Interviewer, or work directly with an expert through Interview Query’s Coaching Program to refine your approach and stand out in the McKinsey & Company data analyst hiring process.