Az álláshirdetés lejárt.
Our partner is looking for a Head of Data Science to lead and become part of our growing Data Scientist team. They are a dynamic company shaping the future of online sports betting and gaming. As a leading global brand, they are dedicated to fostering a culture of innovation and excellence, delivering top-tier products and services to their customers worldwide.
Responsibilities
- Management of data science projects and services. Including contributing expertise and business context to project planning and implementation and oversight of the performance of data science models deployed in production.
- Identifying new opportunities where data science can deliver business value, particularly through automating or enhancing account-level decision-making (e.g., bonus-abuser detection).
- Line management of the Data Science team. Including leading data science sprints and retrospectives, providing support and expertise to the team, and fostering a culture of continuous improvement and agile delivery.
- Collaborate with Data Engineering (within BI) to ensure resilient deployment of models and smooth integration with both our technical infrastructure and operational processes across the business.
- Hands-on modelling work, including building prediction models to identify accounts matching certain behaviour patterns (e.g., trading and compliance risks) and content automation and personalisation models (including recommendation engines).
Requirements
- Proven experience leading data science teams in a commercial environment, including experience of agile environments (e.g., sprints and retrospectives) and pragmatic stakeholder management.
- Deep understanding of the full model lifecycle, from problem scoping and data exploration through to deployment, monitoring and iteration. Track record of delivering robust production-ready batch and real-time models that fulfil business goals, including operationalising them in a business setting.
- Thorough understanding and experience of different ML techniques, notably supervised regression and classification models (Deep NN, XGBoost, GLM), unsupervised learning methods (clustering, principal components) and reinforcement learning (actor-critical, temporal difference). Understanding of model-selection techniques and model diagnostics. Familiarity with modern data engineering and MLOps practices is desirable.
- Detailed knowledge of querying data in SQL engines (ideally in BigQuery or similar database engines) and in NoSQL engines (e.g., MongoDB and Firestore). Including the ability to work with complex data structures and very large data volumes. Advanced Python or R for data manipulation and machine learning. Working knowledge of data visualisation tools.
- Excellent communication skills are essential, including a high level of written and spoken English.
Benefits
- Knowledge of sports betting and casinos and familiarity with gaming data is desirable.
What they offer
- Opportunity to work remote
- Cafeteria
- Private medical insurance