Senior Data Scientist, GrabX at Kudo

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

23 Jun, 26

Salary

0.0

Posted On

25 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Causal Inference, Experimentation, Statistical Modelling, A/B Testing, Time-Series Analysis, Statistical Methodology, Data-Driven Decisions, Platform Development, Metric Reliability, Experimental Design, Python, R, Spark, Ray, Distributed Computing, AI Agents

Industry

Software Development

Description

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 The GrabX team, a part of Grab's Tech Infra, builds and maintains the experimentation platform used across all Grab's marketplaces. We help product and engineering teams make data-driven decisions by providing tools for A/B testing, statistical analysis, and measurement of business-critical metrics. Our mission: Enable reliable, scientifically sound experimentation for all product teams Develop causal inference and statistical methodologies to measure impact accurately Ensure the integrity, scalability, and performance of the core experimentation platform We are looking for a Senior Data Scientist to join the GrabX team and lead the data science strategy for our experimentation platform—bringing statistical rigor and innovative methodologies to drive platform evolution and reliable decision-making across Grab. Get to Know the Role You'll shape the future of experimentation at Grab. You'll optimize and develop A/B testing methodologies, ensure metric reliability, and grow the platform's capabilities for complex experimental designs. This includes developing solutions for platform contamination, sample ratio mismatch, and network effects, all within our distributed infrastructure environment. You'll report to the GrabX Engineering Manager and work onsite at Grab's One North Singapore office. The Critical Tasks You will Perform You will design, implement, and validate statistical and causal inference methodologies to improve the accuracy and efficiency of A/B tests (e.g., variance reduction, sequential testing, quasi-experimental methods). You will partner with platform engineers to translate statistical concepts into production-ready features within the GrabX experimentation platform. You will define and standardise key business and product metrics, establishing monitoring and validation pipelines to ensure data quality and metric reliability for all experiments. You will provide guidance to product teams on experimental design, power analysis, and result interpretation—conducting complex experiments and communicating findings to both technical and non-technical stakeholders. You will develop and implement solutions for automated experiment analysis, using AI agents or MCPs to grow data-driven insights. Qualifications What Essential Skills You will Need Quantitative Research Background: You have completed graduate-level coursework (Master's) in Statistics, Econometrics, Applied Mathematics, Computer Science, Operations Research, or a related quantitative field, with at least 3 years of professional experience applying statistical methods to real-world problems. This foundation enables you to develop novel methodologies for A/B testing and causal inference. Causal Inference and Experimentation: You have professional experience or academic publications in Causal Inference, Experimentation, Statistical Modelling, or Time-Series Analysis. You can design experiments that account for complex phenomena like network effects and sample ratio mismatch in large-scale environments. Statistical Model Development: You have experience developing, deploying, and validating statistical models and metrics within a platform or high-scale data environment. You can write production-quality code that processes billions of events while maintaining statistical validity. Programming and Distributed Computing: You write code in Python or R, and you have worked with distributed computing systems or scalable data processing platforms (e.g., Spark, Ray, StarRocks). You can implement statistical algorithms that run across distributed clusters. Technical Communication: You can explain complex statistical concepts to engineers and business stakeholders in clear terms. You can document methodologies and present findings through reports and presentations to allow informed decision-making. 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. Balance personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours 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.

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Responsibilities

The Senior Data Scientist will design, implement, and validate statistical and causal inference methodologies to enhance the accuracy and efficiency of A/B tests, while partnering with engineers to integrate these concepts into the core experimentation platform. They will also define and standardize key metrics, provide guidance on experimental design, and develop automated analysis solutions.