Senior Data Scientist (Search & Recommendations)
Reads like a ML Engineer role: putting models into production.
Singapore, Singapore
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingDesign and implement deep learning models for search ranking and session-based recommendations
Fine-tune and deploy LLMs to improve search query understanding and recommendation relevance
Lead offline evaluation design and online A/B tests before production release
Partner with software engineers to scale models from prototype to production
Monitor production models and build retraining pipelines as data patterns change
From the employer’s posting
design and implement deep learning algorithms for search ranking, session-based recommendations, and multi-objective personalization systems
fine-tune and deploy Large Language Models (LLMs) to improve search query understanding and recommendation relevance
lead offline evaluation design and online A/B testing to validate model performance
partner with software engineers to scale your models from prototype to production
monitor model performance in production and implement retraining pipelines
What you’ll bring
All qualificationsCore experience
- Master's in Computer Science, Operations Research, Statistics, or equivalent quantitative field, or equivalent practical experience building production ML systems at scale.
- 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.
Qualification wording
Master's in Computer Science, Operations Research, Statistics, or equivalent quantitative field, or equivalent practical experience building production ML systems at scale.
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.
Education & alternatives
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.
Tools in this posting
- Python
- Scala
- Hugging Face
- PyTorch
- TensorFlow
- Transformers
Source — Tool mentions in context
- 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.
- 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.
- 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
About Grab
Grab is Southeast Asia's leading superapp.
In the employer’s words · Read in context
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 14, 2026 · 17 days ago
- Recorded sightings
- 117
- Last seen by us
- Oct 31, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
Complete your application on jobs.smartrecruiters.com. The employer’s form will show what is required.
Already applied? Track this application
Job description
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.
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Singapore, Singapore
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
See how this role fits your experience
Add your resume to compare the role’s scope, tools and requirements with your experience.