Full Time Data Scientist (Analytics), OmniCommerce Jobs, in Grab Singapore - Ricebowl

Get to know the Team 

The OmniCommerce team is a close-knitted team with diverse backgrounds, sharing a common purpose - to build the best products to engage our customers online and offline. The team is bonded over the passion for building user-first products, driven by numbers and analytical thinking. The Analytics team’s mission is to use data and experimentation to drive product and business innovation. We focus on providing data-driven insights to deliver an impeccable and seamless experience for our consumers, merchants, and driver-partners.

Get to know the role 

We are looking for a Data Scientist (Analytics) to join our OmniCommerce Analytics team and help turn data into decisions across deals, reservations, loyalty, and CRM — the products that drive offline footfall to our merchants. You will work closely with product managers, data scientists, engineers, and stakeholders to run experiments, surface insights, and build the dashboards and analyses that guide day-to-day product decisions. This is a high-ownership, high-learning role: you will be trusted to move fast, close your own knowledge gaps, and grow into an independent analytical partner for the team.

The Day-to-Day Activities 

• Product Analytics: Partner with the product, design, and engineering teams to derive insights, support experiment design, and inform key product decisions.
• Strategic & Business Analytics: Support strategic initiatives with data analytics and insights, including deep-dives into customer and merchant behaviour, product efficacy, and ad-hoc projects.

Your role will be to:

• Run A/B tests and analyse experiment results, translating them into clear, actionable recommendations for product iterations.
• Support experiment design by helping to define hypotheses and success metrics alongside product managers and senior analysts.
• Conduct deep-dive analyses of user and merchant behaviour to uncover trends, patterns, and root causes.
• Build and maintain intuitive dashboards that give stakeholders real-time visibility into product and feature performance.
• Contribute to defining and tracking key business metrics, and proactively flag anomalies or opportunities.
• Process and distill stakeholder requirements into concise insights through reports, presentations, and dashboards.
• Apply AI tools to accelerate your analytics workflow, with a critical eye that validates AI-produced output against raw data, business logic, and regional context.

The Must-Haves

• At least 1 years of experience in a data analytics, business intelligence, or data science role, ideally in a B2C or internet business with large, complex datasets.
• Fluent with SQL and comfortable querying large relational databases; proficient in Python (or R) for analysis and problem-solving.
• Working knowledge of statistical methods, with hands-on experience designing and analysing A/B tests, plus an understanding of hypothesis testing.
• Strong data visualization and storytelling skills, with experience creating dashboards using tools like PowerBI or Tableau.
• Strong communication skills — able to make data clear and actionable for non-technical stakeholders.
• A fast learner who closes their own knowledge gaps, metabolises feedback quickly, and takes ownership of their work in a fast-paced, ambiguous environment.
• Curiosity and a keen eye for spotting patterns and trends in data, with the instinct to hypothesise problem statements and propose appropriate solutions.

Good-to-Haves

• Exposure to machine learning techniques and their application to large datasets.
• Familiarity with building or maintaining data pipelines / ETL.
• Experience in OmniCommerce, e-commerce, retail, loyalty, or marketing analytics domains.

• Bachelor's or Master's degree in Statistics, Analytics, Economics, Mathematics, Engineering, or other quantitative subjects.

• 1–3 years experience in Analytics, Business Intelligence or Data Science, preferably in an internet company with large, complex, high-velocity data.

• Solid SQL writing skills and experience querying large relational databases.

• Ability to distill data and articulate an actionable point of view to non-technical audiences using presentations and visualizations.

• Ability to handle multiple priorities and solve ambiguous problems in a fast-paced environment.

• Hands-on experience with data analysis tools like R or Python and visualization platforms like Tableau / PowerBI.

• Experience designing, running, and analysing A/B tests and product experiments, with an understanding of hypothesis testing and the basic principles of DoE.