Open to opportunities

Jenna Webb

Trust & Safety operator who ships products.

10+ years living at the intersection of risk, data, and strategy— turning behavioral signals, regulatory frameworks, and operational KPIs into programs that actually move the numbers. Across email, messaging, fintech, and SaaS, my most recent Staff PM work used cross-functional, data-driven detection frameworks to cut abuse incidents by 71%. Off-hours, I ship full products end to end across three lanes: detection, live entertainment, and quantitative trading.

96%
False-negative rate cut on recent T&S engagement
71%
Abuse incidents cut as Staff PM in a Trust & Safety org
350h
Per month of manual review automated on recent T&S engagement
10+
Years in Trust & Safety
See the workGet in touch

About

Operator first,
builder always

I spent the last decade-plus running Trust & Safety and fraud programs at scale — most recently a Staff Product Manager role leading abuse-prevention strategy for a global email + messaging platform, before that a Fair Billing Compliance program at a major retail-financial-services firm, and most recently a consulting engagement designing onboarding-fraud detection for a notification platform. I’m fluent in the operational side: SQL, Snowflake, Looker, Splunk, behavioral analytics, root-cause investigation, KPI design, carrier and cross-functional escalation.

What sets me apart is that I don’t stop at the spec — I build the thing. The projects below are products I designed, built, and shipped end-to-end using modern AI tooling to move at solo-builder speed without losing the rigor I bring from the T&S side.

The combination is rare: an operator who can talk to legal, finance, and carriers, then go write the schema. That’s the role I’m looking for next.

Selected projects

Three lanes, one builder

Detection, entertainment, and trading — different domains, same end-to-end ownership. Each project below was designed, scoped, and shipped by me.

Detection & Verification

Trust & Safety tooling

Fraud-detection APIs, domain risk scoring, and OSINT verification — tools shaped by ten years of frontline T&S work.

  • Domain Risk API (SDAT v2)

    Shipped

    Commercial fraud-detection API

    The successor to the Config-checker prototype. Multi-tenant SaaS modeled on industry leaders like eHawk: an API-first product where customers automate fraud decisions instead of running them through a human-review console. The initial version of this engine was installed into a prominent email provider’s infrastructure during a fraud-detection consulting engagement, validating the scoring model on real submission traffic. v2 wraps that proven analyzer in a multi-tenant API with a cross-customer fingerprint reputation network and community feedback loop.

    • Initial version installed into a prominent email provider’s infrastructure for live fraud detection
    • Wraps the proven Config-checker analyzer instead of rewriting it
    • API-first: sync POST /submissions today, async-compatible response shape for tomorrow
    • Django 5
    • Postgres
    • Python
    • REST API
    • +1
    Try the live demo→

  • Config Checker (SDAT v1)

    Shipped

    Streamlit prototype that scores domains for fraud risk

    The original Streamlit-based domain-risk analyzer (~400KB Python). Accepts a domain, returns a structured DomainApprovalResult with risk score, recommendation, and an investigator-friendly summary. Still in active use; the engine that the v2 API now wraps.

    • Single-domain risk scoring with a structured, queryable result object
    • Pulls DMARC, RDAP, and threat-intel signals into one report
    • Investigator-friendly summary explaining the score
    • Python
    • Streamlit
    • RDAP
    • DMARC
    • +1
    Try the live demo→

  • Domain Risk Checker

    Shipped

    The evolved analyzer

    The next iteration of the Config-checker engine, built around two upgrades. First, dual scoring: a platform-as-tenant link like payhip.com/b/<id> is analyzed twice — once for the clean platform root and once for the specific seller page that actually carries the risk — returning an independent verdict for each, so a bad tenant on a trusted host can no longer hide behind a clean root. Second, a consumer-harm pipeline that detects intrusive pop/push-ad networks, fake-virus scareware, browser-lock traps, and crawler cloaking, surfacing a plain-English consumer summary with the raw signal evidence tucked into an analyst-only panel.

    • Dual scoring: separate risk verdicts for the registrable root and the exact submitted URL/subdomain
    • Catches bad platform tenants (e.g. a scam payhip.com/b/<id>) that root-only checks rate as clean
    • Consumer-harm detection: pop/push-ad networks, scareware, browser-lock, and crawler cloaking
    • Python
    • Streamlit
    • RDAP
    • DMARC
    • +1
    Try the live demo→

  • Social Media Verify

    Shipped

    Streamlit OSINT toolkit

    Enter a domain or email address. The tool auto-discovers social media profiles from the company's own site, verifies each one resolves and references the domain, scans reviews across Google/BBB/Trustpilot/Glassdoor/G2/Yelp/Capterra, detects developer signals (GitHub, npm, Product Hunt, app stores), and computes a 0-100 risk score with Low/Medium/High/Critical tier.

    • Social-profile auto-discovery from a company's own website first
    • Review-site coverage across 7 major platforms (Google, BBB, Trustpilot, etc.)
    • Developer/startup signal detection (GitHub, npm, Product Hunt, Crunchbase)
    • Python
    • Streamlit
    • BeautifulSoup
    • WHOIS
    • +1
    Try the live demo→

Live Entertainment

Platforms for venues, performers, and patrons

Bar-game platforms, booking marketplaces, and mobile games built for real venues under Tangled Webb Entertainment.

  • Tangled Webb Bar Platform

    Beta

    Live bar entertainment platform plus a public marketing site

    A full multi-game platform for venues. Players join by scanning a QR code, hosts run live rounds from a unified console, and admins manage venues, question banks, promotions, performer profiles, and booking flows. Anti-cheat detects when players leave the app mid-question. The same Next.js app serves tanglewebb.com — the public marketing site with about, services, schedule, venues, gallery, blog, and testimonials.

    • Five live game modes share one player join flow and host console
    • Player, host, admin, and performer roles each get a tailored interface
    • QR-driven team join, per-venue leaderboards, drink-special promotions
    • Next.js 16
    • React 19
    • TypeScript
    • Turso (libsql)
    • +2
    Try the live Open Mic demo→

    Game pages on tanglewebb.com

  • GigHive

    Beta

    Mobile-first booking marketplace where bars and venues book live entertainment

    Two-sided marketplace for live entertainment. Performers publish availability and get booked; bars find acts (especially last-minute) by act type, radius, and region. Includes urgent bookings, fill-in subs, an equipment marketplace, classifieds, and a free public events feed with a follow system.

    • 10 act types, location matching by radius and region
    • $5/month subscription with beta-tester bypass; Stripe handles billing
    • Urgent / fill-in board for last-minute coverage
    • Expo (React Native)
    • Supabase
    • Stripe
    • TypeScript
    View the pitch deck→

  • 2 Drink Memories

    Prototype

    Bar-friendly photo-hunt game

    First title from Fly Trapp Games (a sub-label of Tangled Webb Entertainment). Token-based plays via in-app purchase, image pairs generated through Gemini 2.5 Flash, hotspot hit-detection on tap. Designed for the bar context — short rounds, easy to pick up, hard to put down after two drinks.

    • AI-generated image-pair pipeline with hotspot coordinates
    • Token wallet (10/$1.99, 30/$4.99, 100/$12.99) wired for IAP
    • Tappable hotspot detection with miss-marker feedback
    • Expo SDK 54
    • TypeScript
    • Gemini 2.5 Flash
    • Supabase (planned)
    Play the web build→

Algorithmic Trading

Signal-mining for markets

Same hypothesis-test-iterate discipline I bring to abuse detection, applied to equities momentum and volatility setups.

  • Gap Up + Fade Screener (v1)

    Shipped

    Pattern-mining predecessor to V9

    The first algorithmic screener I shipped. Configurable thresholds for the premarket gap (>2%) and intraday fade (<-1%), a parallel ThreadPoolExecutor scanning ~90 tickers, and a sortable result table with CSV export. The work that taught me how this signal actually behaves at scale — and what V9 needed to do better.

    • Configurable premarket-gap and intraday-fade thresholds
    • Parallel scan over ~90-ticker small-cap universe; custom watchlist supported
    • Sortable hit table with CSV export
    • Python
    • Streamlit
    • yfinance
    • pandas
    • +1
    Try the live screener→

  • ML Momentum Screener

    Shipped

    Most advanced of the three screeners

    The current production model. Scrapes today's Finviz news per ticker (with a sub-20-minute 'breaking' flag), layers in pre-market and intraday flow signals, and ranks the universe by a composite score. ~1,700 lines of orchestrated screening logic — the iteration on V9 that added external catalyst awareness to the technical signals.

    • Live Finviz news scrape with 'breaking' flag for <20-minute headlines
    • Multi-timeframe momentum (premarket, intraday, 3D, 10D)
    • Composite scoring blending technicals with news catalysts
    • Python
    • Streamlit
    • yfinance
    • BeautifulSoup
    • +2
    Try the live screener→

  • V9 Momentum Breakout Screener

    Shipped

    Real-time multi-signal momentum scanner

    An algorithmic equities screener I built to apply the same signal-mining discipline I used in trust & safety to financial markets. The V9 model fuses six independent technical signals into a single conviction score, ranks the universe live (alphabetical, randomized, or live-volume-ranked), and auto-refreshes every 60 seconds with audio alerts on watchlist hits. Powers the manual side of an end-to-end trading pipeline that includes backtesting, risk controls, and live execution.

    • Six-signal model: EMA10, RSI(7), 3D & 10D momentum, 10D relative volume, VWAP + order flow
    • Scans up to 2,000 tickers with a thread pool; 60-second auto-refresh
    • Three universe modes including live volume-ranked construction
    • Python
    • Streamlit
    • yfinance
    • pandas
    • +2
    Try the live screener→

Experience

Career highlights

Full CV available on request.

Independent Security & Risk Consultant

Notification-platform engagement

Jan 2026 – Apr 2026

★ Built a domain-risk tool that closed a 68% vendor gap
  • Used signal analysis to surface that the incumbent third-party vendor was only catching ~32% of fraudsters — the other ~68% were getting through hijacked-domain attacks the vendor wasn't built to detect.
  • Built a domain-risk analyzer (SDAT — see the Domain Risk API project below) from scratch to target that exact gap, then partnered with Engineering to integrate it into the platform's vetting pipeline — turning a single-vendor pass into a full third-party-plus-supplemental vetting program.
  • Made it ops-configurable on purpose — scoring weights, rules, thresholds, and taxonomies all live in a UI so the compliance team can pivot to new fraud trends without an engineering release cycle.
  • That integration drove a 96% reduction in false-negative rate while holding false-positive rates flat — the signal-health metric I was optimizing for.
  • Same pipeline cut ~350 hours/month of manual review effort and improved enablement time by 90%, with full automation set to push both further.
  • Designed the behavioral risk scoring and identity-verification frameworks behind it — combining device intelligence, document authenticity, network reputation, and behavioral signals.

Staff Product Manager, Trusted Communications

Twilio Inc.

Jun 2022 – Apr 2025

  • Led Trust & Safety strategy for email and messaging abuse prevention protecting millions of users globally.
  • Designed detection and enforcement frameworks that reduced abuse incidents by 71% while minimizing impact to legitimate senders.
  • Built monitoring dashboards and reporting workflows in SQL, Looker, Tableau, Splunk, and Snowflake — used for executive reporting on enforcement, abuse trends, and policy effectiveness.
  • Investigated phishing, spoofing, malicious-link propagation, account compromise, and large-scale spam campaigns.
  • Collaborated cross-functionally with Security, Engineering, Legal, and carrier partners to deploy scalable anti-abuse controls.

Staff Messaging Compliance Program Manager

Twilio Inc.

Nov 2020 – Jun 2022

  • Led compliance and abuse-prevention strategy for A2P messaging ecosystems (10DLC, Short Code, Toll-Free).
  • Built automated enforcement workflows and carrier policy controls that reduced messaging violation rates by 40%.
  • Owned the weekly compliance operating rhythm — triaging carrier developments, escalating critical issues to senior leadership, and driving cross-functional remediation.
  • Defined KPIs and operational dashboards tracking violation frequency, MTTR, escalation trends, and enforcement effectiveness for executive reporting.
  • Improved compliance review throughput by 60% YoY; reduced average incident resolution time by 35%.
  • Developed training materials and internal documentation that scaled regulatory expertise across Product, Engineering, and Operations.

Compliance Specialist

Twilio Inc.

Jun 2020 – Nov 2020

  • Defined the original escalation workflows, monitoring procedures, and enforcement documentation that became the foundation of Twilio's compliance ops.
  • Designed the first-cut tooling and training materials for high-risk-account review — the patterns the team adopted as canonical for suspicious-traffic triage.
  • Served as escalation lead for compliance incidents, customer investigations, and enforcement response coordination — the operating model that informed the Staff PM role that followed.
  • Recognized with the Magic Owl Award for building the compliance department from the ground up.

Fair Billing Compliance Program Manager

Alliance Data (now Bread Financial)

Apr 2012 – Aug 2018

  • Founded and operationalized the Fair Billing compliance program: fraud identification, dispute resolution, ACH/Reg E.
  • Conducted risk assessments and root-cause analyses that reduced billing discrepancies and tightened operational controls.
  • Supported CFPB audits through reporting, operational analysis, and remediation planning.

Also worth noting

  • Quantitative Trading (Apr 2025 – present): Python data pipelines and algorithmic trading strategies across equities, futures, and options — backtesting, risk controls, and live execution.
  • Recognition: Superb Owl Award (Twilio, 10DLC compliance & carrier direct connections), Magic Owl Award (Twilio, building the compliance department from the ground up), President’s Circle nomination (Alliance Data / now Bread Financial, creating the debt-settlement function).
  • Core stack: SQL, Python, Snowflake, Looker, Tableau, Splunk, Jira, event stream analysis, behavioral analytics.

How I work

Operator’s instincts, engineer’s output

End-to-end ownership

Schema, API, mobile, web, billing — I’m comfortable owning the full stack and the product decisions that come with it.

Move fast, with rigor

AI-assisted development lets me ship at solo-builder speed without skipping the thinking — schemas, tests, and architecture still get the time they need.

Real problems

Every project here came from a real pain point — fraud teams drowning in signups, performers hunting gigs, venues looking for better bar nights.

Calm collaboration

I came up in operations. I can talk to legal, finance, and customers — not just engineers — and I write down decisions so the team can move on.

Contact

Let’s talk

I’m open to roles where I can keep building end-to-end and where my range — fraud, payments, marketplaces, live events — is an asset, not a curiosity. Happy to share deeper walkthroughs of any of the projects above.

jwebb@naanyas.comLinkedIn

Full resume available on request.