
11 - 50 employees
Founded 2014
🎲 Gambling
🤖 Artificial Intelligence
🤝 B2B
💰 $6.9M Series B - Swish Analytics on 2019-05
Gambling • Artificial Intelligence • B2B
Swish Analytics is a machine-learning driven sports-odds and predictive-data company that builds a real-time odds origination engine for sportsbooks and fantasy platforms. It provides hyper-accurate player prop pricing, match and team markets, in-play and pre-game projections, and automated bet-lifecycle management to power bookmaking, bet builders, parlays, and micro-markets. Swish positions itself as a B2B provider focused on automation, accuracy, and scalable odds infrastructure for the sports betting industry.
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11 - 50 employees
Founded 2014
🎲 Gambling
🤖 Artificial Intelligence
🤝 B2B
💰 $6.9M Series B - Swish Analytics on 2019-05
Gambling • Artificial Intelligence • B2B
Swish Analytics is a machine-learning driven sports-odds and predictive-data company that builds a real-time odds origination engine for sportsbooks and fantasy platforms. It provides hyper-accurate player prop pricing, match and team markets, in-play and pre-game projections, and automated bet-lifecycle management to power bookmaking, bet builders, parlays, and micro-markets. Swish positions itself as a B2B provider focused on automation, accuracy, and scalable odds infrastructure for the sports betting industry.
• Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products. • Develop contextualized feature sets using specific domain knowledge in soccer. • Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models. • Strive to constantly improve model performance using insights from rigorous offline and online experimentation. • Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts. • Adhere to software engineering best practices and contribute to shared code repositories. • Document modeling work and present to stakeholders and other technical and non-technical partners.
• Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area • Demonstrated experience developing models at production scale for soccer or sports betting • Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods • Minimum of 3+ years of demonstrated experience developing and delivering effective machine learning and/or statistical models to serve business needs in sports or sports betting • Experience with relational SQL & Python • Experience with source control tools such as GitHub and related CI/CD processes • Experience working in AWS environments etc • Proven track record of strong leadership skills. • Has shown ability to partner with teams in solving complex problems by taking a broad perspective to identify innovative solutions • Excellent communication skills to both technical and non-technical audiences.
• Remote work options
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