A builder and strategist who thrives in high-stakes environments. Background spans product operations, customer support, platform engineering, and AI automation — giving a unique lens on how to scale systems without scaling headcount. Equally comfortable diving into code, running SQL queries, building AI workflows, or facilitating cross-functional strategy sessions.
Trusted by: Disney, Apple, Mattel, Booz Allen Hamilton.
Open to: Head of Product, VP Product, Head of Operations, VP Operations, technical leadership, and strategic advisory roles. Industries of interest: Media & Entertainment, B2B SaaS, Fintech, Consumer Technology, Platform & Infrastructure.
Contact: teddyjatkins@gmail.com | linkedin.com/in/teddy-atkins | github.com/teddyjatkins
Category: Platform Operations. Led cross-functional initiative to automate content distribution at Venice Music. Scaled from 500 to 1,500 monthly distributions, increased per-person output 200%, achieved 50% operations automation, 60% reduction in content errors, 2-hour content publishing turnaround, and avoided $300K+ in headcount costs. Stack: Python, Airflow, AWS Lambda, S3, CloudFront.
Category: Platform Operations. Redesigned support strategy with AI. Handled 10x user growth with same 3-person team, processing 10,000+ monthly tickets at 70% deflection rate. Reduced average response time from 12 hours to 2 hours. Built 200+ knowledge base articles with 50K monthly views. Stack: Zendesk, Dialogflow, Python, PostgreSQL, React.
Category: Platform Operations. Recovered $150K revenue within 90 days, reduced fraudulent accounts by 85%, processed $2M+ monthly transactions at 99%+ accuracy, maintained 99.9% platform uptime. Stack: Python, Scikit-learn, PostgreSQL, Datadog, Stripe API.
Category: Music Technology / Vertical SaaS. Status: In Development. Audio analysis pipeline extracting Musical Fingerprint per track using Essentia and EffnetDiscogs models. Tiered Curator Playlist Database (1,000+ playlists, Tier 1–5). Designed for independent artists and boutique music management teams pitching to playlists. Stack: Python, PostgreSQL, pgvector, Essentia, EffnetDiscogs, FastAPI.
Category: Quantitative Trading. Status: Validation Phase. Data pipeline polling NWS forecast data and Kalshi market snapshots across 20 U.S. cities. Ladder curvature analysis to identify structural inefficiencies in KXHIGH contracts. Stack: Python, Kalshi API, NWS Forecast API, NumPy, Pandas.
Category: Quantitative Trading. Status: In Development. Three-signal architecture: release calendar signal, streaming velocity from kworb.net, market-model probability gap. Targets KXSPOTIFYD and KXSPOTIFYGLOBALD contracts. Stack: Python, Kalshi API, kworb.net, Pandas, scikit-learn.
Category: Consumer Product. Status: Beta Launch Pending. Real-time grocery price comparison with AI-powered product matching and NLP normalization pipeline. Stack: React, Firebase, Python FastAPI, OpenAI Embeddings, Vector Search.
Category: B2B SaaS. Status: Client Onboarding Pending. Lightweight alternative to legacy loan origination software (Encompass) targeting small-to-medium property management firms and boutique lenders.
AI & Automation: OpenAI GPT-4, Claude, Gemini, Zapier, Make, Airtable, ML Operations, Human-in-the-Loop AI.
Product & Operations: Notion, Figma, Confluence, Zendesk, Mixpanel, Jira, Looker, YouTube Content ID, TikTok MediaMatch.
Technical & Data: SQL (PostgreSQL, MySQL), REST APIs, AWS (S3, Lambda, CloudFront), Git, Datadog, Stripe API, Spotify API, Apple Music API, React, Node.js, Python, microservices architecture.
Reading: "The Cold Start Problem" and "Co-Intelligence." Following First Round Review, Product Growth, Not Boring, Music Ally.
Building: Tools for creators, operators, and traders — exploring intersection of music, markets, and commerce.
Exploring: Vertical SaaS in media & entertainment, prediction markets, human-in-the-loop AI systems, robotics, nuclear power.