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EY

Large Enterprises

EY (Ernst & Young) is one of the "Big Four" global professional services firms, providing assurance, tax, consulting, and advisory services. It is one of the largest accounting and professional services networks in the world, operating across numerous countries with hundreds of thousands of employees.

8 interview notes · updated Jul 2026

EY·Data Scientist·Onsite - Behavioral / Leadership

Jul 2026
EY data scientist interview that went deep on program ownership across the full lifecycle. One question, but it had a lot of layers and I wasn't fully prepared for how specific they wanted me to get.
  • Walk me through a program you fully owned from start to finish, covering strategy, planning, requirements, design, development, testing, and rollout. For each phase, what artifact did you produce, what was the riskiest assumption you had to validate, how did you know the phase was done, and what happened when a dependency slipped on the critical path?

“This question is brutal if you haven't actually owned something end to end.”

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EY·Data Scientist·Technical Phone Screen

Jul 2026
Interviewed for a Data Scientist role at EY and got hit with a pretty layered question about how my background actually maps to the job requirements. Not a casual chat.
  • Walk us through how your education and work experience align with what this role requires. Pick one specific course you took and one concrete deliverable you produced in the last two years, and for each explain the technique you used, what you built, and what measurable impact it had. If there are gaps, lay out a 90-day plan with real milestones to close them.

“This question is doing a lot of work.”

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EY·Data Scientist·Onsite - System Design / Architecture

Jul 2026
EY data scientist interview that was basically one big system design question dressed up as a cloud architecture problem. More depth than I expected for a DS role, felt more like a data engineering panel at times.
  • Design a cloud data ingestion and serving architecture that handles both streaming and batch workloads. Walk through your choice of ingestion pattern (CDC, event sourcing, or micro-batch), how you'd partition and compact data, how you'd guarantee idempotency and handle schema changes like adding optional fields, how you'd tokenize PII, and what cost controls you'd put in place. Also describe an incident scenario you'd use in chaos testing.

“This was basically a 45-minute conversation crammed into one question.”

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EY·Data Scientist·Onsite - System Design / Architecture

May 2026
EY data science interview that was basically one massive system design case study about building a cloud data platform for a retail bank. Dense question, lots of moving parts, felt like I was presenting a consulting engagement proposal rather than answering an interview question.
  • Design a cloud data platform for a mid-tier retail bank operating under PII and data-residency constraints. Walk through the full architecture: ingestion, storage, governance, transformation, and consumption layers.
  • How would you define and enforce data contracts and lineage tracking across this platform?
  • What SLAs and SLOs would you set for this platform, and how would you handle partitioning, backfill scenarios, and late-arriving data?
  • What cost guardrails would you put in place, and how would you structure a three-wave rollout with measurable success criteria?
  • What is the one capability you would deliberately defer in this rollout, and what's your reasoning?

“I started with the residency constraints because ignoring those upfront would've made the whole architecture wrong.”

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EY·Data Scientist·Technical Phone Screen

May 2026
EY data scientist interview that threw a pretty gnarly domain knowledge question at me. One question, heavily cross-domain, and it required knowing actual financial services internals rather than just generic data modeling concepts.
  • Pick two financial services domains (from Consumer, Commercial, Capital Markets, Risk & Compliance, Finance, Enterprise, Life Insurance, P&C Insurance, Wealth, or Regulatory Reporting) and compare them across three dimensions: the core data model entities and grain, the typical control points and reconciliations, and the most common failure modes. Then propose a reusable pattern or schema that spans both domains and explain where it breaks down.

“This was a lot.”

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EY·Data Scientist·Onsite - System Design / Architecture

May 2026
EY data scientist interview that went deep into data modeling territory. The whole session was basically one long architecture question broken into parts, which I wasn't fully expecting going in.
  • Design a logical data model for Accounts, Trades, Positions, Limits, and Customers. Walk through the entity relationships, primary/foreign keys, and cardinalities at an ERD level.
  • What normalization vs. denormalization tradeoffs would you make in this model, and why?
  • How would you handle slowly changing dimensions for the Customers entity?
  • How would you expose curated datasets for analytics consumers, and what are the tradeoffs between a star schema approach versus a data lakehouse architecture?
  • Describe the read path you'd design for three different consumers: business dashboards, regulatory data extracts, and ad-hoc analyst notebooks.

“I started drawing out the entities and immediately second-guessed whether Positions should be a derived entity or a first-class table.”

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EY·Data Scientist·Technical Phone Screen

May 2026
EY data scientist round, basically one big SQL problem that looked manageable until I started actually writing it. The deduplication and idempotency requirements together made it way more involved than a typical aggregation question.
  • Given a trades table with duplicate records per trade ID (late-arriving updates), accounts, customers, and risk limits, write SQL to build a daily exposure fact table at the (account, trade date) grain. Requirements include deduplicating to the latest ingested row per trade, computing gross and net notional, a limit utilization ratio, a breach flag, filtering out canceled trades and accounts with failed KYC, and making the query idempotent for backfills. Then explain one edge case your SQL intentionally ignores.

“This one took me a second to decompose.”

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EY·Data Scientist·Hiring Manager Screen

Apr 2026
EY data scientist interview, looks like a senior-level screen focused entirely on a single deep-dive behavioral question about financial services impact. One question, very structured expectations, felt more like a case debrief than a typical behavioral round.
  • Walk me through the most impactful initiative you led in banking, capital markets, insurance, or asset management. What business metric did you move, what were the before and after numbers, how did you work across front office, risk, ops, and compliance, what regulatory constraints shaped your approach, what was the hardest trade-off, and what would you do differently?

“This question is a beast.”

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