Key Takeaways

  • 75% of U.S. employers use automated applicant tracking systems to screen resumes before a human reviews them (Harvard Business School & Accenture, 2021)
  • The most common ATS failures are missing keywords, incompatible formatting, and incorrect file types
  • ResumeGeni scores your resume across 8 parsing layers — modeled on the same steps enterprise ATS platforms like Workday, Greenhouse, and Taleo use to evaluate candidates

How ATS Resume Scoring Works

Applicant tracking systems parse your resume into structured data — extracting your name, contact info, work history, skills, and education — then score how well that data matches the job requirements. Many ATS rejections happen because the parser couldn't extract critical fields, not because the candidate wasn't qualified.

LayerWhat It ChecksWhy It Matters
Document extractionFile format, encoding, readabilityCorrupted or image-only PDFs fail immediately
Layout analysisTables, columns, headers, footersMulti-column layouts break field extraction
Section detectionExperience, education, skills headingsNon-standard headings cause sections to be missed
Field mappingName, email, phone, dates, titlesMissing contact info is a common cause of immediate rejection
Keyword matchingJob-specific terms, skills, certificationsKeyword overlap affects recruiter search visibility and ATS scoring
Chronology checkDate ordering, gap detectionReverse-chronological order is expected by most ATS
QuantificationMetrics, numbers, measurable outcomesQuantified achievements help human reviewers and some scoring models
Confidence scoringOverall parse quality and completenessLow-confidence parses get deprioritized in results

Frequently Asked Questions

Is ResumeGeni free?
Yes. ResumeGeni is currently in beta — ATS analysis, scoring, and initial improvement suggestions are free with no signup required. Full guidance and saved reports may require a free account.
What file formats are supported?
PDF, DOCX, DOC, TXT, RTF, ODT, and Apple Pages. PDF and DOCX are recommended for best ATS compatibility.
How is the ATS score calculated?
Your resume is processed through an 8-layer parsing pipeline that extracts structured data the same way enterprise ATS platforms do. The score reflects how completely and accurately your resume can be parsed, plus how well your content matches common ATS ranking criteria.
Can ATS read PDF resumes?
Yes, but not all PDFs are equal. Text-based PDFs parse well. Image-only PDFs (scanned documents) and PDFs with complex tables or multi-column layouts often fail ATS parsing. Our analyzer will flag these issues.
How do I improve my ATS score?
Focus on three areas: use a clean single-column format, include keywords from the job description naturally in your experience bullets, and ensure all sections (contact, experience, education, skills) use standard headings.

ATS Guides & Resources

Built by engineers with 12 years of experience building enterprise hiring technology at ZipRecruiter. Last updated .

AI-native SDR

Polar Analytics · New York

Who we are

Polar is the complete data platform for omnichannel commerce. We connect every data source a brand runs on - Shopify, Amazon, NetSuite, Meta, Google, Klaviyo - into a single Snowflake warehouse, layer a commerce semantic layer on top, then add AI so operators can ask questions, get answers, and automate workflows without writing SQL.

Our founders came from Turo and Airbnb in Silicon Valley. They built data platforms at scale and wanted to bring that level of sophistication to fast-growing commerce brands. We support 4,000+ merchants, and zero direct competition with a better solution. We serve brands like Quadlock, gorjana, Joseph Joseph, and ARMRA Colostrum.

We shipped MCP integrations with Claude and ChatGPT, AI automations, and an AI Data Engineer that builds connectors on demand. Our positioning: the data layer to build agent workflows for commerce. Customers tell us things like "this is a dream come true - it feels like the first time they showed me Shopify".

How we operate


We publish our operating principles internally and we mean every word. Here are the ones that matter most if you're thinking about joining:

Customer Obsession. Every decision starts with: does this make our users' lives better? If the answer isn't clear, go talk to a customer before you build anything.

Own the Number. Every metric has an owner. If it's yours, know it cold - the trend, the why, the plan. Don't wait for someone to ask. If it's off track, you should be the first to say so.

Raise the Pace. Always ask: what would it take to do this in half the time? Speed is our edge. We try 100 things while the competitor tries one.

Don't Fail Silently. If it's broken, say it. If you're stuck, raise your hand. Hiding problems is the one thing that will actually get you in trouble.

Here to Win, Not to Be Right. Quiet ego, loud standards. Don't fight to be right - fight to win together. Be ruthless on quality, never rude about it.

Optimize for Polar, Not Your Function. "Not my scope" doesn't exist here. If it makes us win, it's your scope.

We're a remote-first team that runs daily standups, ships weekly, and holds ourselves to a standard most companies talk about but don't enforce. We're transitioning from founder-led intensity to systematic company intensity - which means we need people who can maintain the pace autonomously, not just when someone's watching.


Why this role exists


We have two high-intent lead channels generating hundreds of warm signals per week - and no one dedicated to building the systems that convert them.


Our CEO's LinkedIn is a pipeline machine. One recent post generated 1,154 engagements, 317 from Shopify brands, and 40+ meetings in a single day. Regular posts pull 455+ qualified leads per month (45% C-Suite and Founders). Viral posts hit 2,000+ comments. On top of that, our AI-powered cold outbound campaigns touch 3,000 companies per month and generate ~118 workable replies. The infrastructure exists - Clay enrichment, Instantly sequences, AI-scored contacts - but it needs an operator who can run the system, diagnose what's breaking, and make it better every week.

We've learned something important: signal-based campaigns consistently crush broad AI-generated outreach. Manual, personalized LinkedIn DMs outperform automation every time. The person who thrives here isn't just comfortable using AI tools - they're building and iterating on AI workflows to make themselves 10x more productive, while keeping the human judgment that makes the difference between a reply and a delete.

This is not a traditional SDR seat. You're part pipeline operator, part growth engineer.


What you'll own

  • The LinkedIn-to-pipeline engine. Scrape qualified commenters from David's posts via Clay, enrich with company data, score by GMV, and prioritize who gets a DM. Then write personalized outreach that converts.

  • The outbound reply workflow. AI-powered campaigns touch 3,000 companies per month via Instantly. You own all positive and neutral replies- respond within hours, qualify, and route to AEs. You'll also diagnose why reply rates change and iterate on targeting and copy

  • AI workflow optimization. You'll inherit Clay enrichment pipelines, AI-scored contact lists, and Instantly sequences - and you're expected to make them better. Swap models when costs are wrong (we've gone from GPT-4 at $0.20/contact to GPT-3.5 Mini at $0.01). Fix persona targeting when it drifts. Build new signal-based campaigns when you spot a pattern. The infrastructure is there - you're the operator who turns it into a machine

  • Conversion intelligence. You sit on the richest signal data in the company - who replies, who books, who closes, and why. You'll flag patterns in objections and engagement, refine lead scoring based on what actually converts, and help the team understand which campaigns and signals are worth doubling down on

Who you are

We don't have a rigid checklist of requirements. We're looking for a specific kind of person:

  • You've spent 1-4 years as an SDR or BDR in B2B SaaS, ideally selling to ecommerce, DTC, or marketing teams. You know the difference between a sequence that gets replies and one that gets ignored

  • You're a proven LinkedIn operator. Not just connection requests - real DM-to-meeting conversion at scale. You understand tone, timing, and personalization in a way that can't be templated

  • You're an AI-native operator. You've used Clay, ChatGPT, Claude, or similar tools to build real workflows - not just experimented with them. You've automated parts of your own process and can explain what worked and what didn't. If you've vibe-coded an internal tool or built a Clay table from scratch, we want to hear about it

  • You've worked with outbound email tools (Instantly, Outreach, Salesloft, or similar) and you're comfortable working replies, not just sending sequences. You can also diagnose why a campaign's reply rate dropped and fix the targeting or copy

  • You have strong copywriting instincts. Our outreach is educational and value-driven, not template spam. You write messages that sound like a human who understands the prospect's world - and you know when to let AI draft and when to write it yourself

  • You're comfortable with data. You can read Snowflake dashboards, understand conversion funnels, and spot what's working vs what's not. You use data to decide what to change in your own workflows, not just to report results

  • You know the Shopify/DTC ecosystem - or you're deeply curious about it.


What separates A-players

You think in systems but execute with human judgment. You build the Clay table that scores 2,000 LinkedIn commenters by GMV - and then you write the DM that converts the top 50 because you know a personalized message from a real person beats AI-generated outreach every time. You're the person who notices that signal-based campaigns outperform evergreen ones, digs into the data to prove it, and then rebuilds the workflow before anyone asks. You're intellectually curious about the product - not because someone told you to learn it, but because you actually want to understand why ecommerce brands care about semantic layers and MCP integrations. And you're coachable - we're actively iterating on playbooks and you absorb new approaches fast.


Your toolkit

HubSpot (CRM and pipeline), LinkedIn Sales Navigator (research and DMs), Clay (lead enrichment, scraping, AI scoring), Instantly (cold email sequences and reply monitoring), Surfe (HubSpot-LinkedIn sync), Snowflake dashboards (funnel metrics, read-only), and AI models (Claude, GPT) for copy generation, contact scoring, and workflow automation. You'll also use HeyReach for LinkedIn sequencing and Store Leads for enrichment. You need to be proficient in most of these or learn fast - and more importantly, you need to be the kind of person who improves the stack, not just uses it.



What makes this role different

  • You're not cold-calling 100 people a day. Your leads come from a CEO's LinkedIn audience (45% C-Suite/Founders) and AI-scored outbound replies. You're converting warm intent, not creating it from scratch

  • Direct revenue attribution is unusual for an SDR seat. This means you have real skin in the game and real upside

  • Small team, no layers. Direct line to the CEO and Head of Sales. Real autonomy to shape how this role - and its systems - evolve

How we hire

We believe the best people want to go through a demanding process. We've learned the hard way that great interviewers aren't always great operators - so our process is designed to see how you think, not how you present.

1. Motivation screen - A quick call to understand what drives you and whether there's mutual fit


2. Live case study - A real scenario where you work through a problem in real time. No prep decks, no take-homes. We want to see how you actually operate


3. Leadership conversations - Meet the team, understand the culture, make sure this is somewhere you want to build


Our hiring bar: if this person started a company, would we want to join them?

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