Product Manager Resume Examples

Product Manager Resume Example

Great PM resumes highlight outcomes (revenue, retention, activation), prioritization, and stakeholder leadership. Show business context and trade-offs, not just features shipped.

Ideal Structure

1. Summary: Domain focus (fintech, B2B SaaS), market size, user segment, platform (web/mobile)
2. Key Competencies: Discovery, roadmap, OKRs, analytics, experimentation
3. Experience: Outcomes tied to KPIs (ARR, NRR, DAU/MAU, conversion)
4. Notable Launches: 2–3 high-impact launches
5. Education/Certs: MBA, CS, data courses as relevant

Impact Bullets (Examples)

• Increased self-serve conversion +22% by redesigning onboarding and adding progressive profiling.
• Drove +14% NRR via usage-based pricing and premium add-ons.
• Reduced churn −19% for SMB segment through lifecycle messaging and success playbooks.
• Shipped HIPAA-compliant data export, unlocking $3.2M enterprise pipeline.

ATS Keywords

Roadmap, OKRs, JTBD, Discovery, PRD, A/B testing, Cohorts, Retention, Activation, Monetization, Pricing, Stakeholder Management, SQL, Mixpanel/Amplitude, Product Analytics, Go-to-market, ARR, NRR, Churn, Adoption.

Sample Summary

Product Manager with 6+ years in B2B SaaS (data & workflow). Led discovery and GTM for two $1M+ ARR products; improved activation by 22% and NRR by 14% using experimentation and pricing.

Mini Template (HTML)

<h1>Alex Kim</h1>
<p>Remote • alex@product.dev • linkedin.com/in/alexpm</p>

<h2>Summary</h2>
<p>PM focused on B2B SaaS analytics. Shipped pricing + onboarding wins impacting ARR/NRR.</p>

<h2>Core Competencies</h2>
<p>Discovery, Roadmaps, OKRs, A/B Testing, Pricing, Product Analytics (SQL, Amplitude)</p>

<h2>Experience</h2>
<h3>DataFlow — Product Manager (2022–Present)</h3>
<ul>
  <li>+22% signup→activation via onboarding experiments (p<0.05).</li>
  <li>+14% NRR via usage-based pricing and premium add-ons.</li>
</ul>
                    

Common Mistakes

• Feature lists without business impact.
• Jargon without numbers (no KPIs or cohorts).
• Skipping discovery and research artifacts entirely.