Data Scientist

About the role

As a Data Scientist at Pragmatic Play, you will engage in applied research, primarily in computer vision and machine learning. Your responsibilities will include designing and prototyping models, framing business problems as testable hypotheses, and collaborating with ML Engineers to bring prototypes into production.

You will be part of a global team that values ambition and collaboration, working to create innovative solutions that enhance the gaming experience for millions of players worldwide. Success in this role means effectively turning objectives into applied ML solutions and making a visible impact on the projects you work on.

Skills that matter here

• Python: You will use Python for developing machine learning and deep learning models.
• PyTorch: Experience with PyTorch is essential for building deep learning models.
• TensorFlow: TensorFlow knowledge will support your work in creating scalable ML solutions.
• computer vision: The role focuses on computer vision applications like object detection and image classification.
• deep learning: Deep learning expertise is crucial for designing advanced model architectures.

Who this role suits

• Candidates should have a strong analytical mindset and enjoy problem-solving.
• You thrive in collaborative environments and are eager to work cross-functionally.
• Experience with large datasets and a hands-on approach to developing ML models is essential.
• A background in gaming or consumer-facing applications will be beneficial.

From the employer

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.

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.