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Grab

Senior Data Scientist (Search & Recommendations)

Grab is hiring a Senior Data Scientist (Search & Recommendations) in Singapore, Singapore. Level rates it ; you can apply on Grab.

Level's read on this job

  • On Level's scale, this data scientist role is Builds AI ●●●●: the job is building AI systems.
  • The posting mentions Hugging Face, PyTorch and TensorFlow among its tools and skills.
  • Level classes it as a senior role.
  • Grab has 436 other open roles on Level.

AI in this role

hugging-facepytorchtensorflowjax
llm-integrationfine-tuningnlp

About Grab and Our Workplace

Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.

Get to Know the Team

You will join our Search and Recommendations team — join a group of machine learning engineers and data scientists who build the algorithms that help millions of users discover food, groceries, and services across Southeast Asia. We work with engineering, product, and business teams to turn data into features that directly impact how users navigate our platform.

Get to Know the Role

You will report into the Senior Data Science Manager, and work onsite at Grab One North Singapore office. You will design and build machine learning systems that power search ranking and personalized recommendations at scale. Your work will span deep learning model development, LLM integration, and production deployment. You will measure your impact through offline evaluation metrics and online A/B test results that directly affect user engagement and business metrics.

The Critical Tasks You will Perform

  • You will design and implement deep learning algorithms for search ranking, session-based recommendations, and multi-objective personalization systems that serve millions of users daily.
  • You will fine-tune and deploy Large Language Models (LLMs) to improve search query understanding and recommendation relevance.
  • You will lead offline evaluation design and online A/B testing to validate model performance, ensuring measurable improvements to user engagement before production release.
  • You will partner with software engineers to scale your models from prototype to production, including model serving optimization and latency requirements.
  • You will translate data insights into concrete product features by collaborating with product managers and business operations teams, defining requirements based on user behaviour analysis.
  • You will monitor model performance in production and implement retraining pipelines to maintain prediction quality as data patterns evolve.

What Essential Skills You will Need

To perform the tasks above, you will need:

  • Master's in Computer Science, Operations Research, Statistics, or equivalent quantitative field, or equivalent practical experience building production ML systems at scale.
  • At least 3 years of experience building and deploying deep learning models for search, recommendation systems, or NLP applications in production environments.
  • Hands-on experience with Python and Scala to build data pipelines and model serving systems that process high-volume user interaction data.
  • Proficiency in at least one deep learning framework (PyTorch, TensorFlow, or JAX) to implement neural network architectures for ranking and sequence modelling.
  • Experience with LLM fine-tuning and deployment using frameworks such as Hugging Face Transformers or similar tools to adapt pre-trained models for search and recommendation tasks.
  • Demonstrated experience with A/B testing frameworks and statistical evaluation methods to measure model impact on user behaviour metrics.
  • Experience deploying models to production using ML serving infrastructure (such as TensorFlow Serving, TorchServe, or cloud-based ML platforms) and optimising for latency constraints.

Life at Grab

We care about your well-being at Grab, here are some of the global benefits we offer:

  • We have your back with Term Life Insurance and comprehensive Medical Insurance.
  • With GrabFlex, create a benefits package that suits your needs and aspirations.
  • Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
  • We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.

What we stand for at Grab

We are committed to building an inclusive and equitable workplace that provides equal opportunity for Grabbers to grow and perform at their best. We consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.

How we rate this

Senior Data Scientist (Search & Recommendations) at Grab rates 99 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

LLM IntegrationFine-tuningNLPHugging FacePyTorchTensorFlowJAX

Also common in data scientist jobs at this level

Pay

Typical data scientist job
$167k

This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs

Questions you could be asked

  1. How have you integrated a large language model into a production application?
  2. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  3. What NLP problem have you worked on, and how did you measure whether it actually worked?
  4. What's a project where you used Hugging Face hands-on?
  5. Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: LLM Integration, Fine-tuning, NLP, Hugging Face, and PyTorch. 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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