Data Analyst

DADACONSULTANTS PTE. LTD.; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

Our client is a well-established internet platform company operating in the logistics and freight-matching space across multiple markets. They are looking for a Data Analyst to join their regional team, supporting data-driven decision-making across business strategy, operations, and risk management.

This role offers exposure to large-scale marketplace data and close collaboration with cross-functional teams including product, operations, and risk strategy.

Qualifications

• Bachelor's degree in Statistics, Mathematics, Computer Science, Economics, or related quantitative field.
• Proficiency in SQL is essential; working knowledge of Python is required; familiarity with Hive/Spark is a bonus.

Responsibilities

• Drive end-to-end data analysis across key business domains including transaction matching efficiency, pricing strategy, freight operations, and platform risk management.
• Identify patterns, anomalies and opportunities in large-scale marketplace data; translate findings into actionable recommendations and strategy proposals.
• Design and evaluate AB experiments, grey-scale releases, and causal inference frameworks to measure impact of product and operational strategies.
• Build and maintain core metrics frameworks and dashboards covering multiple markets, detecting anomalies and diagnosing root causes.
• Support risk and fraud analysis by identifying suspicious behavioural patterns, assessing rule and model effectiveness, and driving improvements.

Skills

SQL

Python

Education

Bachelor's degree in quantitative field

Tools

Hive

Spark

Job description

About the job

About the Role

Our client is a well-established internet platform company operating in the logistics and freight-matching space across multiple markets. They are looking for a Data Analyst to join their regional team, supporting data-driven decision-making across business strategy, operations, and risk management. This is an exciting opportunity to work at the intersection of analytics, product, and operations - with direct impact on platform efficiency, pricing, and integrity. The role offers exposure to complex, large-scale marketplace data and close collaboration with cross-functional teams including algorithm, product, and risk strategy.

Key Responsibilities

• Drive end-to-end data analysis across key business domains including transaction matching efficiency, pricing strategy, freight operations, and platform risk management
• Identify patterns, anomalies, and opportunities in large-scale marketplace data; translate findings into actionable recommendations and strategy proposals
• Design and evaluate A/B experiments, grey-scale releases, and causal inference frameworks to scientifically measure the impact of product and operational strategies
• Build and maintain core metrics frameworks and monitoring dashboards covering multiple markets, proactively detecting anomalies and diagnosing root causes
• Support risk and fraud analysis by identifying suspicious behavioural patterns, assessing rule and model effectiveness (e.g. intercept rate, false positive rate, financial loss), and driving iterative improvements
• Develop reusable analytical frameworks, documentation, and tools to improve team-wide analytical efficiency and knowledge sharing
• Collaborate closely with algorithm, product, operations, risk strategy, and engineering teams - including counterparts at headquarters - to ensure analytical insights are implemented and tracked
• Continuously optimise experimental design methodologies, analytical approaches, and tooling to improve decision-making speed and conclusion reliability

Requirements

• Bachelor's degree or above in Statistics, Mathematics, Computer Science, Economics, or a related quantitative field
• Proficiency in SQL is essential; working knowledge of Python for data processing and analysis is required; familiarity with big data platforms such as Hive, Spark, or equivalent tools (is a bonus)
• Experience in internet platforms, transaction marketplace, logistics, or fintech industries is preferred; prior exposure to risk management or anti-fraud analytics (is a bonus)
• Strong business acumen with the ability to break down ambiguous problems, form hypotheses, validate with data, and translate insights into executable solutions
• High sensitivity to anomalous data, with an ability to identify suspicious patterns across user behaviour, transaction flows, and relational networks