Resume & LinkedIn Optimization
The hidden truth about how Workday’s ATS actually grades your resume—and why you’re being functionally rejected before any human sees your application.
⏱️ Read time: 8 minutes
⚡ KEY TAKEAWAYS
- Workday dominates pharma hiring. Used by 37% of Fortune 500 companies including Pfizer, Novartis, and most major pharmas.
- You’re graded A/B/C/D, not 0-100. Recruiters only review “A” candidates. If you’re graded “C” or “D,” you’re functionally rejected—no human ever sees your resume.
- Machine learning compares you to past successful hires. The system isn’t just matching keywords—it’s predicting your success based on patterns from people who got the job.
The Truth No One Tells You About Pharma Job Applications
I applied to over 100 director-level roles at pharma companies over two years. Twenty-year track record. Multiple launches. Strong performance ratings. Qualified for nearly every role.
Response rate? Less than 5%.
For the longest time, I thought I was doing something wrong. Maybe my experience wasn’t good enough. Maybe I was targeting the wrong roles. Maybe I just wasn’t competitive anymore.
Then I learned the actual truth: I was never being rejected by recruiters. I was being rejected by an algorithm—and I never even made it to a human reviewer.
Here’s what I learned about how Applicant Tracking Systems actually work in pharma—and what you can do about it.
The Workday Dominance in Pharma
Workday is the most-used Applicant Tracking System (ATS) among Fortune 500 companies, with 37% market share . Major pharma companies including Pfizer use Workday for HR, payroll, and recruitment .
What this means for you: If you’re applying to director-level roles at Pfizer, Novartis, Merck, or most other large pharmas, your resume is being processed by Workday. Understanding how this specific system works isn’t optional—it’s the difference between being seen and being invisible.
The brutal reality: 98% of Fortune 500 companies use an ATS to filter candidates . At the director level, you’re competing against 50-200+ applicants per role. The ATS filters most of them out before a recruiter ever logs in.
How Workday Actually Grades Your Resume
Here’s what I wish I’d known earlier: Workday doesn’t give you a score from 0-100. It grades you A, B, C, or D.
The A/B/C/D System (HiredScore Integration)
Workday uses HiredScore’s AI to grade candidates with a simplified A, B, C, D system, where A denotes the highest match between a candidate’s application and the job requirements .
What each grade means:
- Grade A: Highest match. Your profile strongly aligns with successful past hires for this role.
- Grade B: Good match. You meet most requirements but may have gaps in a few areas.
- Grade C: Weak match. You’re missing key requirements or your experience doesn’t align well.
- Grade D: Poor match. Significant gaps or misalignment with the role.
The A, B, C, D scores are never “curved”—if a job has commonly held requirements, there may be many As and Bs; if requirements are uncommon, there may be many Cs and Ds .
⚠️ THE CRITICAL PROBLEM: The Tier System
When recruiters log into the ATS, the default view shows them the highest-ranked candidates first—Tier 1 (A grades). In high-volume roles with dozens or hundreds of applicants, recruiters often only look at the top-ranked ones .
Translation: If you’re graded “B,” “C,” or “D,” a recruiter might never scroll down to see your resume. You weren’t rejected—you were never reviewed. This is called “functional rejection.”
💡 What I Learned: The Mobley Lawsuit Exposed the System
In 2023, job seeker Julian Mobley filed a lawsuit against Workday, alleging that he applied to hundreds of jobs through their ATS and was repeatedly rejected—not by a human, but by the system, which never passed his application along to recruiters .
Mobley is attempting to bring a class-action lawsuit arguing that Workday’s ATS is functionally acting as a staffing company by making hiring decisions on behalf of employers .
Why this matters to you: The lawsuit revealed that many qualified candidates are being filtered out by algorithms before any human review. Understanding how to get an “A” grade isn’t just about optimization—it’s about being seen at all.
How Machine Learning Predicts Your Success
This was my biggest misconception: I thought ATS systems just scanned for keywords. That’s not how modern systems work at all.
What Workday’s Machine Learning Actually Does
Machine learning uses historical data to predict the likelihood of candidate success in a role based on previously identified patterns and relationships in past hirings .
The system analyzes:
- Past successful hires: Who got hired for similar roles? What did their profiles look like?
- Education patterns: Do successful candidates have MBAs? PharmDs? Specific universities?
- Company progression: Did they work at top-tier pharmas? Biotechs? Consulting firms?
- Functional experience: How many years in each function? What therapeutic areas?
- Achievement patterns: Do they quantify results? What scale of responsibility?
- Career trajectory: Upward progression? Lateral moves? Gaps?
Natural language processing recognizes patterns in resume text—for example, good systems understand that “managed team of five” and “led five-person team” convey similar information .
Example: How ML Evaluates a Marketing Director Candidate
For a Customer Service position requiring excellent communication, problem-solving, and fast-paced environment experience, the system scans for keywords related to communication skills like conflict resolution, while the ML model analyzes past hiring data and identifies patterns, like higher success rates for candidates with previous call center experience .
Applied to pharma marketing: If past successful Marketing Directors at this company had:
- MBA from top-tier programs
- 5+ years in oncology
- Launch experience with $500M+ products
- Previous roles at big pharma (not biotech)
- Quantified market share growth
The ML model will score candidates with similar profiles higher—even if they don’t use the exact same keywords. Conversely, candidates without these patterns get lower scores, even if they’re qualified.
What this means for you: You’re not just competing against the job description—you’re competing against the profile of everyone who’s been successfully hired for similar roles in the past. This is why “good enough” experience often isn’t enough.
Tactical Strategies: How to Get an “A” Grade
Based on how the system actually works, here’s what I learned to do differently:
Strategy 1: Match the “Successful Hire” Profile
How to reverse-engineer the profile:
- Check LinkedIn: Look at people currently in that role at the company. What’s their background? Education? Previous companies? Career path?
- Analyze the hiring manager: What’s their background? People often hire people like themselves.
- Look at recent hires: If you can find who was recently hired into similar roles (LinkedIn announcements), study their profiles.
- Identify the pattern: Do they all have MBAs? Big pharma backgrounds? Launch experience in specific therapeutic areas?
Adjust your resume accordingly: Lead with the experiences and credentials that match the pattern. If everyone has launch experience, make sure “Launch Director” is prominent in your most recent roles.
Strategy 2: Quantify EVERYTHING
Why numbers matter to ML: Machine learning models are trained to recognize quantified achievements as markers of high performance. Numbers = evidence.
The formula that works:
Action Verb + Specific Responsibility + Quantified Result + Time Frame + Business Impact
Bad (gets C or D grade):
- Led launch of oncology product
- Managed marketing team
- Developed brand strategy
Good (gets A grade):
- Led US launch of KEYTRUDA (pembrolizumab) in advanced melanoma, achieving $230M Year 1 revenue (120% vs. forecast), capturing 31% market share, and establishing #1 position vs. 3 competitors within 14 months
- Directed cross-functional marketing team of 12 (brand managers, medical affairs, market access) supporting $1.2B oncology portfolio, resulting in 18% YoY growth and 3 successful indication expansions
- Built 5-year brand strategy for mature immunology asset, identifying $150M whitespace opportunity in community rheumatology, repositioning against biosimilars, and delivering 22% script growth despite 3 new competitors
Strategy 3: Use Semantic Matching, Not Keyword Stuffing
Systems like Workday focus more on semantic matching rather than exact keyword matches . The system understands context and related concepts.
What this means:
- You don’t need to repeat “launch experience” 10 times
- The system recognizes “commercialization,” “go-to-market strategy,” and “product introduction” as related concepts
- Context matters more than exact phrases
Best practice: Use the job description’s exact language for core requirements, but use natural language to describe your achievements. Don’t force keywords awkwardly—the ML model will recognize it.
Strategy 4: Format for Workday’s Parsing System
Platforms like Workday use optical character recognition (OCR) to convert your resume into text, then natural language processing (NLP) identifies job titles, company names, dates, and qualifications .
Formatting rules for Workday:
- File format: .docx is safest. Simple PDFs work, but complex layouts can confuse parsing
- Fonts: Arial, Calibri, Times New Roman (no fancy fonts)
- Layout: Single-column, no tables, no text boxes, no graphics
- Section headers: Use standard terms: “Work Experience,” “Education,” “Skills” (not creative alternatives like “My Journey”)
- Dates: Use consistent format (MM/YYYY or Month YYYY)
- Company names: Use official names (not abbreviations)
Strategy 5: Apply Strategically, Not Broadly
My hard-learned lesson: I used to apply to 50+ roles, thinking volume would help. My interview rate was 3%.
When I switched to applying only to roles where I had 100% of the required qualifications and matched the “successful hire” profile, my interview rate jumped to 40%.
The targeting framework:
- Only apply if you have ALL “required” qualifications (missing one tanks your grade)
- Check if you match the profile of recent hires (LinkedIn research)
- Customize your resume for each application (15-20 minutes per role)
- Target 10-15 strategic roles, not 50 spray-and-pray applications
Why this works: Better to get an “A” grade on 10 applications than a “C” grade on 50. You only need one “A” to get the interview.
LinkedIn: Your Always-On ATS Backup
Here’s what surprised me: recruiters often check LinkedIn BEFORE or AFTER looking at your resume. If your LinkedIn doesn’t match or doesn’t impress, they move on.
Critical LinkedIn Optimizations
✅ Headline (Recruiter’s First Impression)
Bad: “Senior Director at Novartis”
Good: “Oncology Marketing Director | $500M+ Launch Experience | Market Access & Payer Strategy | Ex-Novartis, Pfizer”
✅ About Section (Your Value Proposition)
- Lead with your highest-impact achievement (quantified)
- Include 3-5 career highlights with numbers
- List therapeutic areas, functions, and companies
- Keep it scannable—recruiters skim
- Use keywords naturally (not stuffed)
✅ Experience Section (Mirror Your Resume)
- Use the same quantified bullet points as your resume
- Lead with your biggest win for each role
- 3-5 bullets maximum per role (more gets ignored)
- Update whenever you update your resume
✅ Skills Section (Searchability)
- Add all relevant skills (Product Launch, Market Access, Commercial Strategy, Forecasting, Brand Strategy, etc.)
- Get endorsements from colleagues (LinkedIn’s mini-ATS uses this)
- Recruiters filter by skills—missing one = you won’t show up in their search
✅ Recommendations (Social Proof)
- Get 3-5 recommendations from managers or cross-functional leaders
- Ask them to mention specific quantified achievements
- Recent recommendations (within 2 years) matter most
The Brutal Truth
Your twenty years of pharma experience mean nothing if Workday grades you a “C” or “D.” You’re not being rejected by recruiters—you’re being functionally rejected by an algorithm that never gives you a chance. Understanding how the system actually works isn’t optional anymore. It’s the difference between being seen and being invisible.
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