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ATS: Workday

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36 ATS Keywords Inside

EY Machine Learning Engineer Resume Guide (2026): ATS Keywords, Examples & Insider Tips

Jordan Reid — Former Senior Staff Technical Recruiter (Google, Amazon) who has reviewed 12,000+ engineering, product, and business resumes.

Updated November 7, 20269 min readAI + Human ResearchInsider Knowledge

36+

ATS Keywords

for this exact role

Resume Tips

insider-specific

Bullet Rewrites

before vs after

Common Mistakes

to avoid

EY's 'Build a Better Working World' mission attracts thousands of technical applicants. For a Machine Learning Engineer, the acceptance rate is under 2%. The hiring loop evaluates you on two axes: technical execution (model deployment, data pipelines) and client-facing consulting aptitude (business translation, teaming). Your resume must pass a 7-second recruiter scan in Workday, then survive a detailed review by a technical manager who will score you against a rubric emphasizing client impact, repeatable processes, and quantifiable risk reduction. Fluffy claims about 'state-of-the-art models' are immediate auto-rejects.

ATS Insider Intelligence

How Workday Actually Works

Workday at EY parses PDFs and DOCX, but PDFs with complex formatting (two columns, text boxes) cause critical data loss in the 'Skills' extraction field, where recruiters filter first. The system weights keywords in the most recent 3-year experience block 3x higher than a generic 'Skills' list. Recruiters use Boolean search strings like 'Machine Learning AND (Python OR PySpark) AND (Azure OR GCP) NOT (student OR intern)' to filter the pool. Knockout questions for the role are often 'Do you have the right to work in [country]?' and 'Years of production ML experience?'—a 'No' or '<2' triggers an auto-reject before any human sees your resume.

🎯 ATS Keyword Arsenal

EY • Machine Learning Engineer • Workday — Click any keyword to copy it

⚡ Technical Skills

PythonPySparkSQLTensorFlowscikit-learnMLflowFeature StoreCI/CD for MLA/B TestingModel MonitoringData PipelineContainerizationAPI Deployment

🔧 Tools & Platforms

Microsoft Azure MLDatabricksSnowflakeKubernetesDockerAzure DevOpsPower BIGitMLflow

🧠 Behavioral / Soft Skills

Client CommunicationRequirements GatheringCross-functional LeadershipStakeholder PresentationAgile DeliveryMentoringRisk Management

🏢 Domain Expertise

Model Risk ManagementRegulatory Compliance (e.g., SR 11-7)ROI CalculationProcess AutomationClient P&L ImpactProof of Concept (POC)Business Case Development

See how many you're already using 👇

EY's Workday system automatically filters out over 80% of applicants before human review. Scanning your resume against the exact keyword rubric and knockout criteria used for Machine Learning Engineer roles is the only way to guarantee your application reaches a recruiter's screen.

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Expert Resume Tips for EY

Lead every bullet with a client or business outcome, not a technical task.

EY values business translators. Instead of 'Built a forecasting model,' write 'For a Fortune 500 retail client, developed a demand forecasting model that reduced inventory carrying costs by 18% ($4.2M annual savings).' Quantify impact on client Key Performance Indicators (KPIs) like cost savings, revenue uplift, or risk exposure reduction.

Why this matters at EY

The hiring manager rubric for Senior Machine Learning Engineer (Manager/Assistant Director level) explicitly scores 'Business Impact' higher than 'Technical Complexity.' This demonstrates you can justify your work to a client.

Explicitly name EY's preferred tech stack: Azure ML, Databricks, and Snowflake.

EY's global alliance with Microsoft makes Azure ML the default cloud ML platform. Mentioning specific services like Azure ML Pipelines, Azure Databricks for Spark, and integration with Snowflake for data warehousing signals immediate platform familiarity and reduces your ramp-up time, a key concern for project staffing.

Why this matters at EY

Recruiters are instructed to tag candidates with 'Azure ML' or 'Databricks' for high-priority roles. These are knockout keywords in the initial Workday search.

Detail your Model Risk Management and production monitoring process.

In regulated industries (financial services, healthcare), model governance is critical. Describe your work in model validation, tracking metrics like Population Stability Index (PSI) for data drift, and setting up dashboards for performance decay (e.g., 'monitored ROC-AUC drop >0.05'). Reference frameworks like SR 11-7.

Why this matters at EY

EY's Quality & Risk Management (Q&RM) protocols require this. Showing this expertise moves you from a pure builder to a trusted, deployable asset who understands client audit requirements.

Use the 'STAR' format implicitly, but emphasize the 'A' (Action) with named tools and patterns.

For each project bullet, structure it as: Situation (Client/Problem), Action (Technical steps with specific tools, e.g., 'implemented a Feature Store using Feast on Azure to...'), Result (Quantified business metric). Avoid generic verbs like 'leveraged' or 'utilized.'

Why this matters at EY

This mirrors the behavioral interview format and allows the hiring manager to quickly assess the scope, your technical ownership, and your ability to articulate results—a core consulting skill tested in the case interview.

Include a 'Professional Summary' that blends technical depth with consulting language.

At the top, write a 3-line summary: 'Machine Learning Engineer with 5 years of experience building and deploying ML solutions for financial services and retail clients. Expertise in productionizing models on Azure ML, reducing operational risk through robust MLOps pipelines, and communicating technical concepts to C-suite stakeholders. Passionate about using data to drive measurable business outcomes.'

Why this matters at EY

This summary is the first thing parsed by Workday and read by the recruiter. It immediately positions you as a hybrid technical-consultant, which is the exact profile EY seeks for client-serving roles.

Before vs After: Real Bullet Rewrites

These are the exact bullets that get filtered vs. the ones that get through Workday and land interviews.

Gets Rejected

"Built machine learning models to predict customer churn."

Gets Noticed ✓

"For a telecom client, productionized a churn prediction model (XGBoost via scikit-learn) deployed as a real-time API on Azure Kubernetes Service (AKS), reducing monthly churn by 22% and increasing client ARR by $8.5M. Implemented ongoing monitoring using Evidently.ai to track feature drift (PSI < 0.1)."

Why it works: It names the client context, specific model/tech stack (XGBoost, AKS), a direct financial metric (ARR), and a professional monitoring practice (PSI)—hitting all points of EY's technical execution and business impact rubric.

Gets Rejected

"Managed data pipelines for model training."

Gets Noticed ✓

"Designed and orchestrated a scalable feature engineering pipeline using PySpark on Databricks, reducing feature computation latency from 4 hours to 25 minutes and improving data team productivity by 15 hours per sprint. Pipeline was integrated into Azure ML via MLflow for automated retraining."

Why it works: It provides concrete latency metrics (4h to 25m) and a productivity impact, demonstrating efficiency gains for a client team. Mentioning integration (MLflow, Azure ML) shows architectural understanding of EY's preferred stack.

Gets Rejected

"Improved model accuracy."

Gets Noticed ✓

"Increased fraud detection model precision from 0.76 to 0.91 for a banking client by implementing a stacked ensemble (LightGBM + Neural Network) and hyperparameter optimization via Optuna, reducing false positives by 40% and saving an estimated $2M in annual operational review costs."

Why it works: It uses precise ML metrics (precision), names advanced techniques (stacked ensemble, Optuna), and translates the technical lift into a client cost-saving metric—a language EY partners and directors use daily.

Gets Rejected

"Led a team of data scientists."

Gets Noticed ✓

"Led a cross-functional team of 4 data scientists and 2 engineers to deliver a supply chain optimization POC for a manufacturing client within a 10-week agile sprint, presenting results to the CFO and securing a $500K follow-on project. Established Git-based code review and model documentation standards."

Why it works: It demonstrates 'Teaming' and 'Leadership,' core EY values, with specific team composition, timeline, and a direct business development outcome ($500K project). It also shows process rigor, which is critical for managing project risk.

⚡ Insider Counter-Intuition

The most counter-intuitive rule: demonstrating how you *limited* model complexity is more valuable than boasting about the most advanced algorithm. EY serves regulated clients where explainability, audit trails, and maintenance cost matter more than marginal accuracy gains. Citing a decision to use a simpler, more robust model (like Logistic Regression over a deep neural network) due to lower operational risk, faster client approval, and lower compute costs shows mature judgment that aligns perfectly with EY's risk-aware, client-value culture.

Mistakes That Get Machine Learning Engineers Rejected at EY

❌ Listing every ML framework under the sun in the Skills section.

What happens

Workday's recruiter view truncates long skill lists. Keyword stuffing dilutes the signal and can tag you as a junior candidate lacking depth. Recruiters suspect you've never used most in production.

✓ The Fix

Limit the Skills section to 12-15 highly relevant, confidently held technologies. Mirror the stack from the job description and EY's alliances (Azure, Databricks). Weave niche frameworks (PyTorch Lightning, Ray) into project bullets to prove depth.

❌ Omitting the quantifiable business impact of your work, focusing only on technical metrics (e.g., F1-score).

What happens

The hiring manager cannot assess your value to a client. Your resume scores low on the 'Business Acumen' section of the internal scorecard and is deprioritized for interviews.

✓ The Fix

For every technical achievement, add a line explaining what it meant for the client's business. Connect model performance to a KPI: 'Improving recall by 15% translated to identifying an additional $1.2M in monthly fraudulent transactions.'

❌ Using a visually creative resume with icons, charts, or multiple columns.

What happens

Workday's parsing engine will misplace entire sections of text, often jumbling your experience into an unreadable block. The stripped text seen by the recruiter is garbled, leading to instant rejection.

✓ The Fix

Use a single-column, reverse-chronological format with standard headers (Experience, Education, Skills). Save as a PDF from Google Docs or Word. No tables, text boxes, or header/footer graphics.

❌ Applying for a 'Machine Learning Engineer' role with a resume that only describes building Jupyter notebooks and training models.

What happens

You are flagged as an academic or research Data Scientist, not an Engineer. EY needs candidates who can operationalize models. This mismatch leads to a 'No' in the 'Production ML' knockout question filter.

✓ The Fix

Ensure at least 60% of your bullet points describe deployment, scaling, monitoring, CI/CD, pipeline orchestration, and collaboration with engineering/DevOps teams—not just model development.

FAQ: Machine Learning Engineer at EY

Does EY use Workday's resume ranking score, and how can I improve mine?

Yes, EY recruiters see a Workday 'match score' based on keyword proximity and frequency against the job description. To maximize it, mirror the language from the EY job posting verbatim. If it asks for 'MLflow,' write 'MLflow,' not 'experiment tracking.' Place these keywords in your recent job titles (if accurate) and the first bullet of each relevant role, as recency and position are weighted higher.

What is the difference between a Data Scientist and Machine Learning Engineer resume for EY?

EY Data Scientists focus on exploratory analysis, statistical modeling, and client storytelling. Machine Learning Engineers are assessed on software engineering rigor: CI/CD pipelines (Azure DevOps), containerization (Docker), API development (FastAPI/Flask), and cloud infrastructure (Azure ML, AKS). Your resume must emphasize these engineering competencies alongside ML knowledge to pass the screen.

How important are certifications like Azure AI Engineer or Databricks ML Engineer for EY?

Highly important for signaling. EY invests in partner certifications for its teams. Listing an 'Azure AI Engineer Associate' or 'Databricks Certified Machine Learning Associate' certification in a dedicated 'Certifications' section below Education triggers a positive tag in Workday and shows commitment to the platform, often moving your application to a 'Priority' folder.

Should I include personal or open-source ML projects on my EY resume?

Only if they mirror client-work complexity and include production elements. A GitHub link to a well-architected project with Dockerfiles, unit tests, and a README explaining the business problem can help. A simple Kaggle notebook with high accuracy will not. Frame it like client work: 'Open-source project: Built a... to solve... achieving...'

How long do EY recruiters spend reviewing a Machine Learning Engineer resume?

The first screen averages 6-9 seconds. They scan for: 1) Current/Most Recent Job Title, 2) Key Tech Stack keywords (Azure, Python, Spark), 3) Quantified business impacts ($, %), and 4) Evidence of client-facing or cross-functional work. If these four signals are absent in that time, it's a reject.