Hybrid Data Scientist – Recommender Systems Data Scientist – Recommender Systems

DescriptionAbout Us:ARRISE sets the benchmark for service delivery and excellence in the iGaming industry. Playing a key role in the success of its clients, which include Pragmatic Play, a brand relied upon by the world’s biggest online casinos for its cutting-edge products, ARRISE helps to deliver exceptional gaming experiences to millions of players worldwide.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!About YouRevolutionize the Gaming Experience Through Cutting-Edge Recommender SystemsYou are a passionate and self-driven Data Scientist with a keen eye for innovation and a thirst for pushing boundaries. With your deep expertise in recommendation algorithms and machine learning, you thrive in fast-paced environments where excellence is the norm, and continuous improvement is a way of life. You are ready to take on complex challenges, deliver impactful solutions, and contribute to the development of groundbreaking gaming experiences that captivate audiences worldwide.What You Will Do

• 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