Company Description

Carousell Group is the leading multi-category platform for secondhand in Greater Southeast Asia on a mission to make secondhand the first choice. Founded in August 2012 in Singapore, the Group has a leading presence in seven markets under the brands Carousell, Carousell Media Group, Cho Tot, Laku6, LuxLexicon, Mudah.my, OneShift, REFASH and Revo Financial, serving tens of millions of monthly active users. Carousell is backed by leading investors including Telenor Group, Rakuten Ventures, Naver, STIC Investments, 500 Global and Peak XV Partners (formerly known as Sequoia Capital India).

We are a diverse team across Southeast Asia, India, Taiwan, and Hong Kong, united by our goal to create a sustainable and circular future. Through tech-driven platforms and innovation, we empower users to buy and sell pre-loved items with confidence. We leverage cutting-edge technology, including advancements in AI to enhance user experience, safety, and convenience both online and offline. Join us in shaping the future of recommerce, where sustainability and technology come together to create real-world impact.

Job Description

Analyze user activity data to identify scammer and fraud modus operandi, surfacing patterns across listings, accounts, messaging, and transactions
Own the full lifecycle of detection rules on internal risk systems: propose, test, deploy, and iterate — in close collaboration with engineering and operations teammates
Translate investigation findings into concrete detection signals and rule logic; work directly with engineers and data scientists to implement them
Monitor performance of active rules (precision, recall, false-positive rate) and proactively tune based on results
Investigate emerging abuse patterns end-to-end: from data exploration to root cause analysis to recommended response
Partner with operations (trust analysts, CS) to turn frontline observations into analytical hypotheses
Build and maintain dashboards and reports to track fraud trends, rule coverage, and team KPIs
Participate in cross-functional reviews with Product and Engineering on fraud tooling and model development

Qualifications

Competencies & Prior Experience

Analytics & Investigation

Strong SQL skills — comfortable writing complex queries for exploratory analysis and production monitoring
Experience analyzing behavioral or transactional datasets to detect anomalies, patterns, or abuse signals
Logical, structured problem-solving: able to decompose ambiguous fraud patterns into testable hypotheses

Technical Fluency

Working knowledge of how ML/data science models function (not a builder, but a capable consumer and collaborator)
Comfortable with APIs, data pipelines, and understanding what data points drive what model or rule outcomes
At ease in a terminal environment; uses AI tools (e.g., Claude, Cursor) as a natural part of daily workflow

Trust & Safety Domain

Exposure to fraud, scam, or abuse detection in a marketplace, fintech, or platform context preferred
Understanding of common fraud typologies: account manipulation, fake listings, social engineering, payment fraud
Familiarity with rule-based detection systems or risk scoring frameworks

Working Style

Genuinely curious about fraud and adversarial behavior — not just processing tickets, but interested in how scammers operate
Proactive: identifies problems and proposes solutions without waiting to be told
Effective communicator with both technical (engineers, data scientists) and non-technical (operations, leadership) stakeholders
Comfortable with ambiguity and fast iteration in an early-stage fraud tooling environment

Additional Information

By proceeding with your application, you are adhering to our PDPA policies. In case you are interested to know more, read about our Candidates Personal Data Privacy Statement.

By proceeding with your application, you are adhering to our PDPA policies. In case you are interested to know more, read about our Candidates Personal Data Privacy Statement.

Data Analyst (Trust & Safety)