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Open nowPosted 9 days ago

Senior Data Scientist (Search & Recommendations)

Grab75 open roles

Where
Singapore
Work mode
On site
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Your applicationOpen nowSenior Data Scientist (Search & Recommendations)Grab · Singapore

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. Grab postings stay open a median of 14 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 9 days ago

Grab median: 14 days open

The posting

Company Description

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.

Job Description

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.

Qualifications

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.

Additional Information

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.

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