Senior Data Scientist (AI)

TL;DR

Senior Data Scientist (AI): Designing and developing advanced AI/ML and Generative AI solutions for Razer Gold payment services with an accent on LLM fine-tuning, RAG systems, and Agentic AI. Focus on building robust AI pipelines, automating business processes, and deploying scalable models to enhance digital content payment experiences globally.

Location: Must be based in Singapore

Company

Razer is a global leader in gaming hardware and services, dedicated to revolutionizing the gaming experience through innovative technology and a gamer-centric culture.

What you will do

• Translate complex business problems into effective AI/ML and GenAI solution designs.
• Develop end-to-end AI solutions including data preparation, model training, and performance evaluation.
• Apply advanced GenAI technologies to drive process automation and operational efficiency for Razer Gold.
• Collaborate with engineering and product teams to build and deploy robust AI product pipelines.
• Communicate data-driven insights and complex AI findings to stakeholders and technical leadership.

Requirements

• Master's or PhD degree in Computer Science, AI, Data Science, or a related field.
• 2+ years of professional experience in the GenAI/AIML domain.
• Proficiency in Python and SQL with deep learning frameworks like PyTorch or TensorFlow.
• Experience with LLM fine-tuning, RAG systems, Agentic AI, and reinforcement learning.
• Must be located in Singapore.
• Experience with cloud-based AI deployment (AWS, GCP, or Azure) and big data frameworks like Spark.

Nice to have

• Previous working experience in fintech, payment systems, or fraud detection.
• Experience with fraud and financial crimes compliance AI initiatives.

Culture & Benefits

• Global impact working on products for millions of gamers worldwide.
• Environment focused on accelerated personal and professional growth.
• Inclusive, gamer-centric workplace culture that values diversity and fair opportunity.
• Exposure to cutting-edge AI technologies and large-scale data challenges.