← netflix / Product Manager, Ads (Targeting)
tailored_resume_v2 / art_1PN9u3KNDks
role
model
anthropic/claude-sonnet-4.6
created
2026-07-28T00:21
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What changed for netflix
| change | why it matters |
|---|---|
| Summary rewritten to lead with enterprise platform scale (675M+ engagements, 50K TPS) and signal ingestion/taxonomy language | JD's first requirement is owning ad tech platforms at scale with signal management and data taxonomy expertise |
| Intuit reordered to lead experience section and first bullet leads with 675M+ scale metric | Intuit is the strongest proof of enterprise platform infrastructure at scale, directly mapping to JD's platform vision and multi-stakeholder roadmap requirements |
| Intuit Asterias bullet reframed around 'data taxonomy and signal classification standards' | JD explicitly requires deep experience with data taxonomies, signal classification, and audience data infrastructure |
| Intuit MSaaS Drift Detection bullet reframed as 'governance frameworks for data quality and platform compliance' | JD requires owning standards for signal governance and compliance with evolving advertising regulations |
| Fintellect RAG pipeline bullet reframed as 'signal ingestion and normalization architecture' | JD requires signal ingestion, normalization, taxonomy design — RAG pipeline is the closest factual analog |
| Fintellect domain-specific agents bullet reframed as 'contextual signal taxonomy' | JD requires audience and contextual signal taxonomy design for advertiser outcomes |
| aeval project moved to lead the projects section | aeval's governance frameworks, signal quality standards, and safety gates are the strongest analog to the JD's data governance and signal quality requirements |
| aeval bullets reframed to explicitly reference governance frameworks, signal quality, classification standards, and compliance pipelines | JD requires owning standards for how targeting signals are defined, classified, and maintained — aeval's eval taxonomy is the closest factual analog |
| Splunk bullets reframed to emphasize enterprise customer focus and platform impact metrics | JD requires proven track record with enterprise advertisers and agencies and measurable platform outcomes |
| Kaiser bullets condensed to 2, reframed around data platform governance and capacity planning | Lower relevance role; retained for data platform at scale credential but condensed to preserve page budget |
| Streamio condensed to 2 bullets focused on multi-agent orchestration and 0-to-1 GTM execution | Lower direct ad tech relevance; retained for platform architecture and GTM credentials but condensed |
| AutoEval and Deep Learning Education Platform projects removed | Lowest relevance to ad targeting platform role; removed to maintain 2-page target |
| Lawrence Berkeley National Laboratory entry removed from projects | Minimal relevance to ad tech targeting role; removed to preserve page budget |
JD analysis (20 key phrases)
Key phrases: Ads Targeting Platformaudience and contextual signalssignal ingestion, normalization, taxonomy designdata governanceforecasting, media planning, and advertiser salesmulti-stakeholder roadmapprogrammatic buyingCTV advertisingDSP platformsenterprise advertisers and agenciestargeting infrastructurecommercial differentiatorcross-functional alignmentplatform vision and strategysignal qualityGTM strategyad serving systemsaudience data platformscampaign activation and optimizationconnective tissue between sales, GTM, engineering, and data science
Hard requirements:
- 10+ years PM experience with significant tenure owning ad tech platforms at scale
- Targeting infrastructure, signal management, or audience data platforms ownership
- Expert-level understanding of DSP platforms, programmatic buying, audience and contextual targeting, CTV advertising
- Deep experience with data taxonomies, signal classification, and audience data infrastructure
- Foundational architectural decisions in partnership with engineering and data science
- Fluency in ad serving systems, SSPs, DSPs, APIs, and integration frameworks
- Executive-level communication and cross-functional alignment across sales, GTM, legal, data teams
- Advertising data governance, privacy regulations, targeting compliance familiarity
Preferred qualifications:
- Strong UX leadership for sales, media planning, and operations teams
- CTV advertising experience
- GTM strategy shaping for targeting products
- Bias for impact, comfortable with ambiguity
Per-role mapping (9 roles scored)
| role | score | reframe angle | JD phrases that map |
|---|---|---|---|
| Intuit — Staff PM Developer Frameworks & Platform Infrastructure | 4/5 | Platform infrastructure at enterprise scale — signal ingestion, API design, data governance, and multi-stakeholder roadmap alignment | multi-stakeholder roadmap, platform vision and strategy, signal ingestion, data governance, cross-functional alignment, API, enterprise scale |
| Splunk — Senior PM Search Orchestration | 3/5 | Data platform ownership with enterprise customer focus and measurable performance outcomes | multi-stakeholder roadmap, platform vision, bias for impact, enterprise advertisers and agencies |
| Kaiser Permanente — SOA Technical PM | 2/5 | Data infrastructure at scale with governance and capacity planning | signal quality, data governance, platform scales with business |
| Fintellect AI — Founder & CEO | 3/5 | Signal ingestion, data taxonomy design, and GTM execution for a data-driven consumer platform | signal ingestion, normalization, taxonomy design, GTM strategy, campaign activation and optimization, audience and contextual signals |
| Streamio AI — Founder & CEO | 2/5 | 0-to-1 platform architecture and go-to-market execution | platform vision and strategy, GTM strategy, bias for impact |
| IBM — Software Engineer BI Products | 2/5 | Enterprise data/BI platform credibility | enterprise advertisers and agencies |
| Bank of America Merrill Lynch — Tech MBA Associate | 1/5 | Quantitative commercial analysis | forecasting, media planning |
| RL Workbench Project | 2/5 | Signal normalization and taxonomy design for benchmarking platforms | signal ingestion, normalization, taxonomy design, data governance |
| aeval Project | 3/5 | Signal quality governance and data platform architecture | signal quality, data governance, targeting infrastructure, campaign activation and optimization |
Tailored summary
Senior Technical Product Manager with 12+ years owning data platforms and multi-stakeholder roadmaps at enterprise scale — including scaling a platform infrastructure to 675M+ engagements and 50K TPS at Intuit, and architecting signal ingestion, normalization, and taxonomy systems across AI and data products. Proven track record setting platform vision, driving cross-functional alignment across engineering, data science, GTM, and commercial partners, and shipping durable infrastructure that serves both technical and commercial needs. NeurIPS published researcher; deep fluency in API design, data governance frameworks, and translating complex data capabilities into advertiser-facing value propositions.