Senior Data Scientist (Consumer Experience)
Grab is hiring a Senior Data Scientist (Consumer Experience) 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 PyTorch, TensorFlow and scikit-learn among its tools and skills.
- Level classes it as a senior role.
- Grab has 436 other open roles on Level.
AI in this role
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
The Consumer Experience team aims to provide a great user experience across the app. The data science team works on problems ranging from ranking and recommendations to agentic LLM chatbots and content generation. We're a hands-on team interested in the end-to-end data lifecycle: from wrangling data to understanding the trade-offs between model complexity and deployment in production. We are looking for an experienced Data Scientist with an AI product mindset to help push the frontier of personalization. If you're passionate about solving complex problems with immediate real-world impact, we want you!
Get to Know the Role
You will focus on enhancing a communications targeting system by developing relevance ranking, translation models, and agentic marketing content generation projects. You will report to the Senior Data Science Manager and this role is based in Grab One North Singapore office. If you are ready to shape how millions of users interact with our platform, apply today!
The Critical Tasks You Will Perform
- You will enhance communication targeting systems by developing relevance ranking algorithms, translation models, and agentic marketing content generators.
- You will analyse quantitative and qualitative data to develop a deep behavioural understanding of our users and inform personalization strategies.
- You will collaborate with product, marketing, and engineering teams to manage the end-to-end lifecycle of designing, implementing, and deploying ML and LLM models.
- You will build and test robust models, develop data pipelines, and establish observability and evaluation frameworks for systems in production.
- You will leverage Generative AI (GenAI) and AI coding assistants to accelerate model development, automate data wrangling, and streamline your evaluation workflows.
What Essential Skills You Will Need
- At least 4 years of experience in data science or machine learning, supported by a Master's degree in Computer Science, Engineering, Mathematics/Statistics, or a related technical discipline.
- Proficiency in programming languages such as Python, R, or Java, along with experience in data pipeline development and ETL processes.
- Experience with predictive modelling algorithms (e.g., logistic regression, neural networks, decision trees, heuristic models) and their trade-offs in production.
- Hands-on experience with ML/DL frameworks and libraries, including Scikit-Learn, Pandas, XGBoost, TensorFlow, or PyTorch.
- Practical experience managing the end-to-end ML lifecycle, including feature selection, hyper-parameter optimization, and model validation.
- You have experience engaging with AI tools and emerging technologies to enhance productivity, improve workflows, and contribute new ideas.
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 enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.
How we rate this
Senior Data Scientist (Consumer Experience) at Grab rates 93 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.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ 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
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
- What's a project where you used PyTorch hands-on?
- Walk me through how you've used TensorFlow in your day-to-day work.
- What are the limits of scikit-learn that you've run into, and how did you work around them?
- What's a project where you used XGBoost hands-on?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: PyTorch, TensorFlow, scikit-learn, and XGBoost. 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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