[Hiring] Data Scientist @ARRISE

Role Description

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.

Qualifications

• 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.

Requirements

• 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.

Benefits

• 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.