Personalisation Data Scientist Intern — ML & A/B Testing

Razer; Singapore; On-site; SGD 13,000 - 27,000; Full time

Job summary

Razer invites a Data Scientist Intern (Personalisation) to join our Big Data team and contribute to production recommendation systems across Razer.com, Synapse, Cortex, and other software platforms. You will collaborate with data scientists and engineers to improve personalisation, accelerate experimentation, and drive user engagement.

You will develop AI-driven personalised recommendations, harness ML/DL techniques, and participate in feature engineering, model prototyping, and offline

Qualifications

• Pursuing a degree in Computer Science, Data Science, Statistics, or a related field.
• Solid ML fundamentals and curiosity to apply them to business impact.
• Hands-on ML project experience (academic or industry).
• Strong Python and SQL programming skills.
• Experience with cloud platforms (AWS, GCP, Azure) is advantageous.

Responsibilities

• Develop and enhance AI-driven personalised recommendation systems.
• Leverage AI-assisted tools to accelerate feature engineering and model prototyping.
• Design and analyse A/B tests and evaluate model performance.
• Improve production pipelines using Airflow and AWS services.
• Monitor, debug, and optimise deployed ML systems.
• Tackle real-world challenges like cold-start and revenue-driven personalization.

Skills

Passion

ML fundamentals

Hands-on ML projects

Python & SQL

ML frameworks

LLM workflows

Analytical & teamwork

Cloud technologies

Education

Pursuing degree in CS/Math/Stats

Tools

TensorFlow

Keras

Scikit-learn

PyTorch

Job description

Razer invites a Data Scientist Intern (Personalisation) to join our Big Data team and contribute to production recommendation systems across Razer.com, Synapse, Cortex, and other software platforms. You will collaborate with data scientists and engineers to improve personalisation, accelerate experimentation, and drive user engagement.

You will develop AI-driven personalised recommendations, harness ML/DL techniques, and participate in feature engineering, model prototyping, and offline