Experienced Quantitative Developer

🕒 il y a 28 jours

🏄 California – Distant

🏄 California – Remote

💵 $170 000 / an

⏰ Temps Plein

🟡 Intermédiaire

🟠 Senior

🖥 Ingénieur Logiciel

🦅 Parrain de Visa H1B

Cette entreprise a parrainé des visas H1B par le passé.

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Swish Analytics

Site WebLinkedInTous les Emplois

11 - 50 employés

Fondée en 2014

🎲 Jeux d'argent

🤖 Intelligence artificielle

🤝 B2B

💰 €6 909 110 Series B - Swish Analytics en 2019-05

Gambling • Artificial Intelligence • B2B

Swish Analytics est une entreprise spécialisée dans les cotes sportives et les données prédictives, alimentée par le machine-learning, qui développe un moteur d'origine de cotes en temps réel pour les bookmakers et les plateformes de fantasy. Elle offre des cotes de joueur extrêmement précises, des marchés de matchs et d'équipes, des projections en direct et pré-match, ainsi qu'une gestion automatisée du cycle de vie des paris pour soutenir les bookmakers, les créateurs de paris, les combinés et les micro-marchés. Swish se positionne comme un fournisseur B2B axé sur l'automatisation, la précision et une infrastructure évolutive de cotes pour l'industrie du pari sportif.

Description

• Architect and build the core trading systems that execute our fair value models across sports betting exchanges at scale • Design event-driven trading systems that consume fair value models and market data to make sub-second execution decisions • Build the core logic for comparing fair values against live market prices and determining when/where to trade • Implement asynchronous order generation, submission, and cancellation workflows across multiple venues with different latency profiles • Design state machines for order lifecycle management (pending, accepted, filled, cancelled, rejected) with proper event ordering and idempotency • Build venue-specific integrations (WebSocket connections to Matchbook, Kalshi; REST API adapters for Betfair; FIX protocol handlers) • Implement intelligent order routing that selects optimal venues based on liquidity, fees, latency, and position constraints • Design coordination logic for managing orders across multiple venues when a single bet spans several platforms • Handle venue-specific quirks (rate limiting, connection drops, partial fills, odds movement during submission) • Build real-time position tracking systems that aggregate exposure across all venues, markets, and event types • Implement global liability management that enforces risk limits while maximizing capital utilization • Design systems that detect and respond to position drift (when actual fills deviate from intended exposure) • Create reconciliation engines that validate positions against venue reports and detect/resolve discrepancies • Design data pipelines that ingest real-time market data from multiple feeds (WebSocket streams, REST polling, custom adapters) into low-latency in-memory stores • Build efficient order book representation and query systems optimized for fast fair value lookups • Implement message ordering and deduplication logic for ensuring consistent state across async operations • Design persistent logging and event sourcing for order/trade auditing and post-incident analysis

🎯 Exigences

• 3+ years building production trading/market-making systems for betting syndicates, sharp groups, or exchanges • Deep understanding of exchange vs. bookmaker dynamics and practical experience executing against both • Hands-on experience integrating with real-time sports betting data feeds and exchange APIs • 3+ years of production Python with expert-level async/await, event loop, and concurrent execution skills • Strong system design for distributed, real-time, event-driven systems • Deep understanding of database transactions, consistency models, and state management under high throughput • Experience with message streaming platforms (Kafka or equivalent) for order/execution event handling • Proficiency with containerization (Docker), orchestration (Kubernetes), and cloud infrastructure (AWS, GCP)