Grab
From Grab's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Get to Know the Role Reporting to the Senior Analytics Manager, you will be the analytics owner.
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 GrabFood Analytics team is part of ACE Analytics, which covers GrabFood, GrabMart, GrabExpress and OmniCommerce. We are the product analytics partners to the GrabFood product organisation across six Southeast Asian markets. Each analyst owns one or two product problem spaces end-to-end. They define the metrics that matter, design and read the experiments behind every launch, and run the deep dives that decide what gets built next. We work AI-first: analysts build and share reusable AI workflows that take on the repetitive parts of the job, so our time goes to judgement and product thinking. This is a new seat, created to take on additional product problem spaces.
Get to Know the Role
Reporting to the Senior Analytics Manager, you will be the analytics owner. You will also be the advisor for one to two GrabFood product problem spaces. These problem spaces may include, for example, ordering experience and conversion, merchant discovery and content, delivery reliability and demand shaping, or affordability and promotions. You will work directly with the product manager, designers, engineers and data scientists for that space, most of whom are based in Singapore, and with country teams across the region.
This is a senior individual-contributor role. You set the metrics. You design an experiment and read it. You make the ship, iterate or stop recommendation and hold it when the data says so. You decide what to prioritise when several partners need you in the same week.
This is a product analytics role, not a machine-learning role: your time goes to metrics, experiments, root-cause analysis and product decisions, not to model building.
We base this role in Petaling Jaya and will require you to work onsite.
The Critical Tasks You Will Perform
You will define the metrics for your problem space: the primary metric at the exact step a feature changes, the leading and lagging indicators around it, and the guardrails that catch the failure modes.
You will design and analyse controlled experiments with your product manager: hypothesis, randomisation unit, success metric, sample size and duration, guardrails, and the checks that keep a result honest. You will turn results into a clear ship, iterate or stop recommendation.
You will diagnose metric movements systematically. This will involve, locating the movement, decomposing it, cutting it by funnel step, segment, platform, and market. After that, ruling out tracking and data-quality causes. Finally, explaining it why.
You will size opportunities and run proactive deep dives into consumer, merchant and driver-partner behaviour that shape the roadmap rather than report on it.
You will oversee the data specifications and instrumentation for new features with engineering, so the data exists to measure what you planned to measure.
You will build the dashboards and certified metrics that give the product team reliable self-serve visibility.
You will build AI-assisted analytics workflows for yourself and the team, validated against raw data and business logic, and share what works.
You will improve on experimentation and analytical rigour across the problem spaces you touch, and coach junior analysts as the team grows.
What Essential Skills You Will Need
At least 4 years in analytics or quantitative roles, with at least 2 years of experience where you owned the metrics for a consumer product, growth, pricing, CRM, or marketing programme. Experience in data analytics is a bonus. It is especially valuable in industries such as food or grocery delivery and e-commerce marketplaces. It is also valuable in ride-hailing and super-apps, e-wallets and digital banking, travel, telco digital products, and consumer FinTech.
Fluent SQL on large datasets, and proficient Python or R for analysis.
Hands-on experience designing and analysing controlled experiments: A/B tests, holdouts, or well-designed quasi-experiments such as difference-in-differences or synthetic control where randomisation was not possible. You can explain why you chose the design, what could bias it, and how you read a result that is not clean.
Sound statistics applied to decisions: hypothesis testing, confidence intervals, sample size and power, multiple comparisons, selection bias and mix shifts. You can explain these in plain language to a product manager, and you know when a method breaks.
Precise metric framing: You can define the right metric for a change. This includes specifying its numerator, denominator, and the population it is measured on. Additionally, you can distinguish a feature-engagement metric from a conversion metric.
Structured solutioning on ambiguous questions: You lay out the space before you commit to a hypothesis, and you can write the analysis plan before you open a notebook.
Manage partner trade-offs: You set priorities across competing asks, say no with reasons, and keep quality on what you ship. You close your own knowledge gaps quickly and act on feedback.
Clear communication: you make the finding and the decision clear to product leaders and engineers alike, in writing and live.
You use AI tools in your supervised analytics workflow, validating output against raw data and business logic.
At least a Bachelor's degree in Statistics, Economics, Mathematics, Engineering, Computer Science, Analytics or another quantitative field.
What Would Be Good to Have
Experience in food delivery, marketplaces or super-apps across Southeast Asian markets.
Experience with an experimentation platform, or having built experiment analysis tooling.
Causal inference methods beyond A/B testing.
Dashboarding in tools like Tableau, Superset or PowerBI; build data pipelines.
Have built a reusable AI workflow, skill or agent that your team adopted.
Coach analysts.
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.
Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours
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, race, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.
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