Data Scientist

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.
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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 Learning, Unsupervised Learning).
• 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.