We are looking for a mid-to-senior Data Scientist who is eager to proactively identify opportunities to improve and automate our business using data science and machine learning. You will work within a small, dedicated team of data scientists collaborating closely with backend engineers, other departments, and end users, while being part of a larger ecosystem of trading and technology experts.
In this role, you will design, develop, and optimize predictive models, implement anomaly detection systems, and participate in building production-grade machine learning solutions that directly impact trading and operational processes. You’ll also get hands-on experience with (or apply your existing knowledge of) high-performance architectures, including Kafka, serverless functions, databases, CI/CD pipelines, Kubernetes, and Docker.
Key Responsibilities:
- Develop and refine mathematical and statistical models for predicting sports and e-sports outcomes.
- Communicate with end users, collect feedback, and build technical solutions based on it.
- Apply machine learning to improve trading strategies, automate workflows, and detect anomalies.
- Proactively identify opportunities to use data science for business optimization and process automation.
- Perform large-scale historical data analysis to generate insights and improve model performance.
- Collaborate with backend engineers to build and maintain production machine learning systems.
- Research and prototype new methods for expanding into new sports and markets.
KPIs for This Role:
- Delivery of production-ready machine learning models and pipelines for trading within agreed timelines.
- Improvement in model accuracy and reliability, measured against established benchmarks.
- Reduction of manual intervention in trading workflows through automation initiatives.
Requirements:
- 3+ years of experience in data science, analytics, or a related field.
- Proficiency in Python (pandas, numpy, scikit-learn, etc.) and working with databases.
- Strong knowledge of statistical modeling, probability, and machine learning techniques (Supervised and Unsupervised).
- Experience with XGBoost/LightGBM.
- Experience with large-scale data analysis and building actionable insights.
- Ability to work collaboratively in a cross-functional team and communicate results clearly.
Nice-to-Have:
- Experience in the sports or betting industry.
- Experience with PyTorch/Tensorflow.
- Familiarity with real-time data processing (Kafka, streaming systems).
- Exposure to building data pipelines and integrating ML models into production systems.
- Knowledge of advanced ML techniques (e.g., ensemble methods, time-series forecasting, anomaly detection).
What We Offer:
- Work on high-impact projects in the fast-growing sports and e-sports betting industry.
- A collaborative environment within a skilled team of data scientists and engineers.
- Flexibility: Hybrid work with remote options.
- Opportunities to shape our analytics and machine learning ecosystem and see your ideas implemented in production.