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Overview
Join Pragmatic Play as a Data Scientist and contribute to innovative ML solutions in the iGaming industry. Work with a global team to develop advanced algorithms and optimize data pipelines.
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, ARRISE helps to deliver exceptional gaming experiences to millions of players worldwide. Our global team of over 10,000 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 10,000 ARRISERS! See a job that excites you? Apply now, and our friendly recruitment team will connect with you soon. Your journey starts here.
WHAT YOU'LL BE DOING
- •Design and develop advanced ML algorithms for image, text, audio, and tabular data
- •Develop and optimize pipelines in Azure to train models across various domains
- •Implement end-to-end applications in Python, including unit and integration tests
- •Continuously monitor system performance and troubleshoot issues
- •Collaborate with cross-functional teams to produce solutions to complex problems
- •Develop common packages and tools for logging, model inference, and more
WHAT WE ASK OF YOU
- •Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- •Strong proficiency in Python, PyTorch, and TensorFlow
- •Proven experience in managing and analyzing large datasets
- •Solid understanding of foundational DL algorithms
- •Expertise in unit and integration tests (Pytest) and CICD pipelines
- •Experience with data versioning, model management, experiment tracking, key evaluation metrics, and optimization techniques
- •Extensive knowledge and hands-on experience in Docker
- •Solid understanding of traditional Computer Vision and classical ML
- •In-depth experience with computer vision models for object detection, image classification, and segmentation
- •Experience with Azure services (ML Studio, Data Factory, Functions, Blob Storage) or equivalent cloud provider services
- •Knowledge of AWS (Amazon Web Services) services, especially EC2 and S3
- •Experience with Huggingface Transformers, Elastic Stack, Gitlab CI pipelines
- •Experience with Prompt Engineering, Retrieval Augmented Generation (RAG), LLM frameworks (LangChain, LlamaIndex, Haystack, ...), and fine-tuning LLMs