at Razer
Full TimeJoining Razer will place you on a global mission to revolutionize the way the world games. Razer is a place to do great work, offering you the opportunity to make an impact globally while working across a global team located across 5 continents. Razer is also a great place to work, providing you the unique, gamer-centric #LifeAtRazer experience that will put you in an accelerated growth, both personally and professionally.
Key Responsibilities:
Lead the design, development, and deployment of machine learning models for fraud detection, customer segmentation, recommendation systems, and other business applications.
Work with large-scale structured and unstructured data to generate insights and optimize decision-making processes.
Collaborate with data engineers and software engineers to build robust data pipelines and deploy AI models into production.
Develop and implement experimentation frameworks (A/B testing, causal inference) to evaluate model performance.
Stay ahead of industry trends and emerging technologies in AI/ML and apply them to Razer Gold’s business needs.
Communicate complex data findings and AI-driven insights to business leaders and technical teams.
Mentor and guide junior data scientists, fostering a culture of continuous learning and innovation.
Requirements:
Master’s or Ph.D. in Computer Science, Data Science, Statistics, or a related field.
5+ years of experience in data science, with a proven track record of deploying machine learning models in production.
Strong expertise in machine learning, deep learning, and statistical modeling.
Proficiency in Python, SQL, and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Experience with big data technologies like Spark, Hadoop, or distributed computing frameworks.
Hands-on experience with cloud platforms (AWS, GCP, or Azure) for ML model deployment.
Excellent problem-solving skills and ability to translate business problems into data-driven solutions.
Strong communication skills, with the ability to work cross-functionally and influence stakeholders.
Nice to Have:
Experience in fintech, payments, or fraud detection.
Knowledge of graph analytics, reinforcement learning, or NLP.
Prior experience leading a team of data scientists or machine learning engineers.
Are you game?
