SIRE

1 Job

2 Employees

About the Company

SIRE leverages prediction markets to generate uncorrelated yield from sports outcomes.

Sports markets operate independently of macroeconomic conditions, providing return opportunities when traditional assets are correlated to the downside. Using proprietary data and quantitative models, we identify mispriced probabilities and execute on-chain with institutional-grade risk management.

For allocators seeking portfolio diversification beyond traditional assets.

Listed Jobs

Company Name: SIRE
Job Title: Lead Data Scientist- Sports Betting ML
Job Description: Job title: Lead Data Scientist – Sports Betting ML Role Summary: Lead the design, development, and deployment of advanced machine learning models that predict sports outcomes for betting strategy. Own end‑to‑end model lifecycle, mentor a team, and ensure models drive profitable betting decisions in production. Expactations: • Deliver production‑ready ML models that outperform current benchmarks. • Build and maintain back‑testing and live monitoring pipelines. • Communicate results to trading, engineering, and executive stakeholders. • Keep the team updated on emerging techniques in sports analytics and ML. Key Responsibilities: • Design, train, and tune deep learning architectures (CNNs, Transformers) on structured sports data. • Lead a small squad of data scientists and data engineers, providing mentorship and technical direction. • Develop robust back‑testing frameworks and validate models against historical data. • Collaborate with trading and engineering teams to integrate models into live betting workflows. • Ensure data quality, pipeline reliability, and real‑time monitoring of deployed models. • Stay current with research in predictive analytics, neuro‑network techniques, and betting market dynamics. Required Skills: • 5+ years in data science/ML, with ≥2 years in a leadership capacity. • Proven success building sports betting or market prediction models. • Deep expertise in PyTorch or TensorFlow, CNNs, Transformers, and other neural network methods. • Advanced Python programming and SQL proficiency. • Strong statistical foundation: probability, hypothesis testing, regression, classification. • Experience deploying ML models to production (containers, A/B testing, monitoring). • Excellent verbal and written communication; ability to explain technical concepts to non‑technical audiences. Required Education & Certifications: • Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Physics, or related quantitative field.