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ABOUT 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 13,000 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 13,000 ARRISERS! See a job that excites you? Apply now, and our friendly recruitment team will connect with you soon. Your journey starts here. ABOUT THE ROLE You will join our Data Science team as a Data Scientist working at the applied-research end of computer vision. You will frame open-ended problems as testable hypotheses, develop prototypes to validate them, and choose the model architectures and methods best suited to each. Computer vision is the core domain, with scope to work across text, audio, and tabular data. Working with ML Engineers, you will help take validated prototypes into production and see them run at scale. Even if you don't meet every requirement, your skills and ability to deliver impact are what matter most. WHAT YOU'LL BE DOING Design and prototype ML and deep learning models, with computer vision as the primary domain (object detection, classification, tracking). Frame business problems as testable hypotheses and develop proofs-of-concept to validate them, iterating on the results. Evaluate and benchmark competing model architectures and pre-trained models to identify the best-fit approach. Collaborate with ML Engineers to scale validated prototypes into production systems and stay engaged through deployment. Track experiments, manage model and data versioning, and define evaluation metrics to compare approaches objectively. Work with product, engineering, and business teams to turn objectives into applied ML solutions. WHAT WE ASK OF YOU Master's degree in a relevant field such as Electrical Engineering, Computer Science, or a related quantitative discipline. Hands-on experience developing ML and deep learning models, including demonstrated computer vision work. Strong proficiency in Python and a deep learning framework (PyTorch or TensorFlow). Experience with computer vision models for object detection, image classification, and tracking. Solid grounding in deep learning, traditional computer vision, and classical ML. Strong experimentation mindset: benchmarking, ablation studies, and structured evaluation of competing approaches. Experience working with large, complex datasets. Nice to have Experience collaborating with ML Engineers to bring models into production. Experience with any major cloud platform (Azure, AWS, or GCP) for ML training and deployment. Familiarity with Docker and CI/CD pipelines. Experience with Hugging Face Transformers, vision transformers, or self-supervised / representation learning. Exposure to generative AI: prompt engineering, RAG, LLM frameworks (LangChain, LlamaIndex, Haystack), or LLM fine-tuning. Background in gaming, iGaming, e-commerce, or other consumer-facing applications at scale. How we work Our stack includes Python with PyTorch and TensorFlow, experiment tracking and model versioning, containerized deployment with Docker, and CI/CD pipelines. Data Scientists and ML Engineers work together across the full lifecycle, from early research through to models running in production. WHAT WE OFFER IN EXCHANGE Work on substantial, real-world computer vision and deep learning problems at scale. End-to-end involvement, from research and prototyping through to production. Opportunities for professional and personal development. A collaborative, cross-functional environment with visible impact. Originally posted on Himalayas