Swish Analytics · 5 days ago
Senior Quantitative Developer
United States
Full-time
Remote
Mid, Senior Level
$150K/yr - $195K/yr
3+ years expSwish Analytics is a sports analytics, betting, and fantasy startup building the next generation of predictive sports analytics data products. The Senior Quantitative Developer will architect and build core trading systems that execute fair value models across sports betting exchanges at scale, focusing on real-time decision-making and low-latency execution.
AnalyticsBig DataFantasy SportsMachine LearningPredictive AnalyticsSports
Responsibilities
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
Qualification
Required
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)
Ability to architect systems that make correct decisions under tight latency constraints
Strong debugging skills for timing issues, race conditions, and event ordering problems
Systematic problem-solving for production incidents in trading systems
Pragmatic engineering decisions (when to accept latency vs. consistency tradeoffs)
Preferred
Experience building order management systems (OMS) or execution management systems (EMS)
Background in low-latency or high-frequency trading system design
Hands-on work with WebSocket real-time connections and connection resilience patterns
Experience with FIX protocol or similar financial messaging standards
Knowledge of multi-leg execution and cross-product coordination challenges
Familiarity with market microstructure (order book dynamics, market impact, slippage models)
Experience designing systems that respond to real-time market feedback (volatile prices, volume spikes)
Company
Swish Analytics
Swish Analytics operates as machine learning system for sports.
11-50 employees
H1B Sponsorship
Swish Analytics has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (1)
2024 (1)
2022 (1)
2020 (1)
Funding
Current Stage
Growth StageTotal Funding
$8.73MKey Investors
Titanium Ventures
2019-05-29Series B· $6.91M
2018-07-03Series Unknown
2016-05-27Seed· $1.8M
Leadership Team
Corey Beaumont
Co-Founder and Head of Engineering
Robert Skoff
Co-Founder
Recent News
The Business Journals
2023-12-23
2023-08-17
Company data provided by crunchbase