Data Scientist - Recommender Systems
Description
Our global team of over 6,000 talented and driven professionals are shaping the future of iGaming. Headquartered in Gibraltar, we have offices spanning Canada, India, the Isle of Man, Latvia, Malta, Romania, Serbia, Bulgaria, and the UAE, and more exciting destinations on the horizon.
At ARRISE, we take pride in creating growth opportunities at all levels, constantly investing in our people while welcoming new colleagues and forging strategic partnerships that open new opportunities for success.
To achieve this, we bet on ourselves. We know that success is a collective effort, and our team is driven by ambition, collaboration, and a shared commitment to grow and succeed—while embracing every step of the journey.
Be part of the future of iGaming with 6,000 ARRISERS! See a job that excites you? Apply now, and our friendly recruitment team will connect with you soon. Your journey starts here!
- Design and develop advanced recommendation algorithms to deliver tailored recommendations to our diverse user base
- Improve the efficiency and scalability of recommendation systems to handle large volumes of data and ensure fast response times
- Conduct rigorous A/B testing to validate and iterate recommender models for optimal performance
- Efficiently handle and integrate data from various sources, including APIs, databases, and Google Analytics
- Collaborate with cross-functional teams across data and engineering to produce solutions to complex problem statements
- Continuously monitor system performance, troubleshoot issues, and implement improvements/optimizations
- Create and maintain dashboards that provide actionable insights into recommender system performance
- Stay updated with the latest trends and advancements in recommender systems and machine learning
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- Strong proficiency in Python, with extensive experience in PyTorch and/or TensorFlow
- Proven experience in managing and analyzing large datasets
- Solid understanding of relational and NoSQL databases
- In-depth experience with Retrieval, Ranking, Batch, Live, and Sequential recommendations
- Knowledge of architectures for recommenders such as Two-Tower, DLRM, DCN
- Excellent problem-solving, analytical, and communication skills
- Experience with NVIDIA Merlin, TensorFlow Recommenders, NVTabular
- Knowledge of unit and integration tests (Pytest), CICD pipelines, data versioning, model management, experiment tracking
- Familiarity with key recommender metrics and optimization techniques, Docker
- Highly competitive salary
- Comprehensive company training on highest standards
- Friendly and supportive culture
- Tremendous growth opportunities in a large, fast-moving international company