Senior Analytics Manager (Payments)

The Analytics team empowers data-driven decision-making across our product and business units within the Grab Financial Services ecosystem. As part of the Payments Tech Family, we play a crucial role in driving cashless payments across all Grab verticals, focusing on both enhancing the payment experience and strengthening core payment capabilities. If you thrive on intellectually stimulating challenges and have a passion for data, you'll find a great fit with us. Are you skilled in data wrangling, modeling, and visualization? Do you enjoy experimenting with statistics and mathematics to discover new insights? If so, we'd love to have you on our team. Beyond technical expertise, we seek individuals who embody our core values—the 4Hs: heart, hunger, honor, and humility.

Get to know the Role

You will lead payments analytics teams, working closely with cross-functional partners (Product, Business, Engineering, Design, and Data Science) to understand data requirements, identify and track key metrics, and provide data-driven insights. You will evaluate the feasibility of new business/product ideas and provide actionable recommendations. This role reports into the Head of Analytics, GrabFin & Platform and is based onsite in Grab Singapore office, offering significant opportunities to shape data strategy and grow as a leader. If this sounds like you, apply now!

The Critical Tasks You Will Perform

• Lead the exploration of business opportunities and formulate data-driven hypotheses to address key challenges, adapting in-production models to new markets to enhance product velocity.
• Collaborate with product and engineering teams to design, execute, and analyze controlled experiments that directly impact key product decisions.
• Develop advanced statistical models (e.g., mixed effect, causal inference) to understand interaction effects and establish anomaly detection systems to monitor product metrics.
• Uphold rigorous standards in measurement and analysis, facilitating the development of analytics tools to assist wider teams in debugging and data summarization.
• Support team members in their professional development, fostering a data-driven culture and coaching analysts on both classical statistical rigor and modern analytics techniques.
• Direct the team's adoption of LLM and GenAI-assisted workflows (e.g., natural language to SQL, automated insights) to enhance the speed and quality of decision-making, while prioritizing opportunities to embed AI capabilities into internal tooling.