← faire / Staff Product Manager, AI, Data, ML & Platform
tailored_resume_v2 / art_FD4X2GyPUec
role
model
anthropic/claude-sonnet-4.6
created
2026-08-28T19:35
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What changed for faire
| change | why it matters |
|---|---|
| Summary rewritten to lead with 'data and ML platforms for internal technical users' framing and 675M+ engagements proof point | JD's first requirement is technical fluency across data and ML platforms with Staff-scope ownership; leading with scale metric establishes credibility immediately |
| Intuit role reordered to lead with 675M+ engagements / 50K TPS bullet rather than SDK bullet | JD emphasizes platform scale, adoption metrics, and ML infrastructure — these are the strongest proof points for Faire's platform PM scope |
| Asterias bullet reframed as 'structured dataset ownership and metadata discoverability' with SQL/BigQuery discovery language | JD explicitly requires metadata quality, lineage, discovery, and structured discovery with data scientists — Asterias maps directly |
| MSaaS Drift Detection bullet reframed as 'data contract enforcement and pipeline health monitoring' | JD requires defining data contracts, driving pipeline health, and establishing dataset ownership — drift detection is the exact analog |
| Streamio AI role reframed to lead with OpenClaw multi-agent orchestration as 'AI data agent workflows' | JD's 'Scale the use of AI data agents' is a primary responsibility; OpenClaw is the strongest proof point for agentic architecture experience |
| Splunk role title adjusted to 'Search Orchestration & Data Platform' and Search Catalog bullet leads | JD requires metadata quality, lineage, discovery, and semantic layers — Search Catalog (PostgreSQL metadata service) and SPL are the most relevant Splunk proof points |
| Splunk RICE framework bullet reframed as 'durable intake process and operating model' | JD explicitly requires building a product-platform operating model with intake process — RICE framework is the direct evidence |
| Kaiser Permanente bullets reframed around SLAs, pipeline health, dataset ownership, and platform cost/efficiency | JD requires SLA definition, pipeline health ownership, and platform cost as a health metric — KP's 1.7TB/day logging service and capacity planning map directly |
| RL Workbench project moved to lead the projects section | JD's primary ML platform requirement is 'training, deployment, observability, and model registry' — RL Workbench covers all four phases with the most technical depth |
| RL Workbench bullet reframed around 'full training, deployment, observability, and model registry workflow' | Exact JD phrase; workbench architecture maps precisely to each component |
| aeval project reframed to emphasize 'trust and reliability gates for model outputs' and CI/CD safety gates | JD requires 'increase trust in Faire's core data' and quality investments — aeval's statistical rigor and automated safety gates are the strongest proof |
| Fintellect role condensed to 3 bullets focusing on RAG pipeline, live market data ML, and compliance | JD values marketplace/data-rich product context where ML drives relevance — Fintellect's Alpaca integration and sentiment pipeline map; other bullets (App Review detail) are lower relevance |
| Bank of America Merrill Lynch role removed from experience section | Summer associate role from 2011 adds no incremental signal for a Staff ML/Data PM role; space better used for high-relevance ML project detail |
| Deep Learning Education Platform project removed | Educational demo project adds no platform PM proof; space allocated to higher-relevance AutoEval project |
| Lawrence Berkeley National Laboratory entry removed from projects | Undergraduate research entry superseded by NeurIPS 2014 citation embedded in BRAIN project bullet; space optimization |
JD analysis (20 key phrases)
Key phrases: ML idea to productionstructured discoveryplatform investmentstraining, deployment, observability, and model registrytime saved, models shipped, and support loadAI data agentsdata-engineering investments agents depend ondecisions acceleratedtrust in Faire's core dataSLAsdata contractsdataset ownershipmetadata quality, lineage, and discoveryaccess controls and durable compliance processessemantic layers, reusable data productsproduct-platform operating modelintake processadoption trackingplatform cost and efficiencysearch, recommendations, or personalization
Hard requirements:
- 8+ years product management experience at Staff or Lead scope
- Technical fluency across data and ML platforms
- Experience shipping platform products for internal technical users
- Experience in marketplace or data-rich product context (search, recommendations, personalization)
- Cross-functional influence and strategic prioritization with senior engineers and data scientists
- Ability to build operating model: discovery, intake, measurement practices
Preferred qualifications:
- ML in production: model training, deployment, feature pipelines, evaluation
- Data contracts, SLAs, pipeline health, dataset ownership
- Metadata quality, lineage, discovery, access controls, compliance
- Agentic data insights and AI data agents
- Semantic layers, reusable data products, developer experience
- Platform cost and efficiency as health metric
Per-role mapping (9 roles scored)
| role | score | reframe angle | JD phrases that map |
|---|---|---|---|
| Intuit — Staff Product Manager, Developer Frameworks & Platform Infrastructure | 5/5 | Platform PM who scaled internal ML/data infrastructure to 675M+ engagements, built developer-facing data products, drove adoption through structured discovery, and established operating models for platform teams | platform investments, structured discovery, adoption tracking, platform cost and efficiency, intake process, product-platform operating model, time saved, metadata quality, data contracts, developer experience |
| Streamio AI — Founder & CEO | 4/5 | Builder of production agentic AI platforms and multi-agent orchestration frameworks — directly maps to Faire's AI data agents vision | AI data agents, ML idea to production, training, deployment, observability, structured discovery, decisions accelerated |
| Fintellect AI — Founder & CEO | 3/5 | ML-powered data-rich product in a marketplace-like context with algorithmic personalization | search, recommendations, or personalization, ML idea to production, feature pipelines |
| Splunk — Senior Product Manager, Search Orchestration | 4/5 | Data platform PM who owned metadata services, query orchestration, and structured prioritization frameworks for internal technical users | metadata quality, lineage, and discovery, SLAs, intake process, structured discovery, platform investments, semantic layers |
| Kaiser Permanente — SOA Technical Product Manager | 3/5 | Enterprise data platform PM with SLA ownership and capacity planning at scale | SLAs, pipeline health, platform cost and efficiency, dataset ownership |
| IBM — Software Engineer, Business Intelligence Products | 2/5 | Early data/BI engineering foundation | — |
| Bank of America Merrill Lynch — Tech MBA Summer Associate | 1/5 | Quantitative data analysis in financial context | — |
| RL Workbench — Post-Training RL Platform | 5/5 | Hands-on ML platform builder covering the full training, evaluation, and benchmarking lifecycle | training, deployment, observability, and model registry, ML idea to production, time saved, models shipped, platform investments |
| aeval — AI Model Evaluation Platform | 5/5 | Built production ML evaluation platform with statistical rigor, CI/CD integration, and observability — directly maps to ML platform quality and trust requirements | training, deployment, observability, and model registry, trust in Faire's core data, SLAs, models shipped |
Tailored summary
Staff-level Technical PM with 12+ years building data and ML platforms for internal technical users — from scaling Intuit's developer platform to 675M+ engagements and 50K TPS, to shipping production RL post-training workbenches and AI model evaluation platforms with full observability pipelines. Deep experience driving ML idea to production, establishing data contracts and platform operating models, and building agentic AI frameworks that accelerate decisions across product and engineering teams. NeurIPS published researcher; UC Berkeley Engineering + CMU MBA.