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Razer

Data Science Intern

Razer is hiring a Data Science Intern in Singapore, Singapore. Level rates it ; you can apply on Razer.

Level's read on this job

  • Level rates this role Builds AI ●●●●: the job is building AI systems.
  • The posting mentions PyTorch, TensorFlow and scikit-learn among its tools and skills.
  • Level classes it as an internship.
  • Razer has 148 other open roles on Level.

AI in this role

pytorchtensorflowscikit-learnkeras
ai-evaluation

Joining 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.

Job Responsibilities :

Main Tasks & Responsibilities:

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

You will:

  • Develop and enhance AI-driven personalised recommendation systems using Machine Learning (ML) / Deep Learning (DL)

  • Leverage AI-assisted development tools to accelerate feature engineering, user segmentation, model prototyping, code generation, offline model evaluation, performance benchmarking and documentation

  • Design and analyse A/B tests and statistically evaluate model performance

  • Enhance production pipelines using Airflow and AWS services

  • Support monitoring, debugging, and optimisation of deployed ML systems

  • Work on real-world challenges including cold-start, personalisation at scale, revenue optimisation, and production infrastructure constraints.

Pre-requisites:

  • Passion and interest in using Data Science to drive business impact

  • Strong foundational understanding of ML fundamentals and core concepts

  • Have hands-on ML project experience (academic or industry)

  • Proficiency in Python, SQL and common machine learning frameworks (e.g. TensorFlow, Keras, Sklearn, Pytorch) and LLM-powered workflows and embeddings

  • Diligent, reliable, strong analytical skills, good communication skills, and teamwork

  • Experience with cloud technologies (AWS, GCP, Azure etc) is advantageous

Pre-Requisites :

Razer is proud to be an Equal Opportunity Employer. We believe that diverse teams drive better ideas, better products, and a stronger culture. We are committed to providing an inclusive, respectful, and fair workplace for every employee across all the countries we operate in. We do not discriminate on the basis of race, ethnicity, colour, nationality, ancestry, religion, age, sex, sexual orientation, gender identity or expression, disability, marital status, or any other characteristic protected under local laws. Where needed, we provide reasonable accommodations - including for disability or religious practices - to ensure every team member can perform and contribute at their best.

Are you game?

How we rate this

Data Science Intern at Razer rates 88 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI EvaluationPyTorchTensorFlowscikit-learnKeras

Questions you could be asked

  1. How do you decide that one model's output is better than another's for a given task?
  2. Walk me through how you've used PyTorch in your day-to-day work.
  3. What are the limits of TensorFlow that you've run into, and how did you work around them?
  4. What's a project where you used scikit-learn hands-on?
  5. Walk me through how you've used Keras in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: AI Evaluation, PyTorch, TensorFlow, scikit-learn, and Keras. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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