Senior Data Scientist

dtcpay; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

dtcpay is building the infrastructure for the next generation of payments where stablecoins, fiat, and digital assets coexist seamlessly. As our first Data Scientist, you will sit at the intersection of compliance, product, and commercial strategy, translating complex data into decisions that shape how we grow, operate, and stay ahead of risk.

This role starts with architecture and data foundation, then expands to product analytics, user behaviour, experimentation, and risk modeling to drive

Qualifications

• 8+ years in data science or machine learning, preferably fintech/fnancial services.
• Proven experience building data infrastructure and pipelines from the ground up.
• Strong proficiency in Python and SQL; comfortable across the full analytics stack.
• Experience with anomaly detection, predictive modelling, graph analytics, or experimentation frameworks.
• Able to communicate findings clearly to non-technical stakeholders.
• Familiarity with AML/CFT concepts, PDPA, or regulated environments is a strong plus.

Responsibilities

• Architect data foundations: build and own data pipelines, governance, and infrastructure for scalable analytics.
• Map and consolidate data across payment, compliance, and operations systems to enable reliable analytics at scale.
• Instrument user behaviour, build acquisition/retention models, and design experimentation frameworks for product teams.
• Analyze transaction performance across channels/geographies/merchant segments to improve conversion and reduce cost.
• Develop the AML and risk intelligence layer with explainable, audit-ready models.

Skills

Python

SQL

Data pipelines

Anomaly detection

Predictive modelling

Graph analytics

Experimentation frameworks

AML/CFT familiarity

Communication skills

Job description

dtcpay is building the infrastructure for the next generation of payments — where stablecoins, fiat, and digital assets coexist seamlessly. As our first Data Scientist, you will sit at the intersection of compliance, product, and commercial strategy, translating complex data into decisions that shape how we grow, operate, and stay ahead of risk.

• This role begins with architecture before analysis. Before models can be built and insights can flow, the data foundation needs to exist — and building it is yours to own.
• You will map and consolidate data across payment, compliance, and operations systems, establishing the pipelines, governance frameworks, and infrastructure that make reliable analytics possible at scale. This includes defining data classification standards, implementing PII detection and masking frameworks, and creating synthetic datasets that keep our analytical environment both powerful and compliant.
• From that foundation, the work expands outward. On the product side, you will instrument user behaviour across merchant and consumer surfaces, build acquisition and retention models, and design the experimentation frameworks that give product teams confidence in the decisions they make. Your outputs here directly shape what gets built and in what order.
• Deeper into the payment stack, you will analyse transaction performance across channels, geographies, and merchant segments — identifying failure patterns, modelling optimal routing logic, and surfacing the signals that engineering and product teams need to improve conversion and reduce cost.
• Across compliance and risk, you will build the intelligence layer that moves our AML and transaction monitoring capabilities beyond static rule engines — developing risk scoring models, anomaly detection systems, and graph-based analytics that identify suspicious patterns at scale while remaining explainable and audit-ready.
• At the commercial level, you will own revenue forecasting, pricing analytics, and the data products that give leadership and regulators a clear view of how the business is performing and where it is headed.

What We're Looking For

• 8+ years in data science or machine learning, preferably in fintech or financial services
• Proven experience building data infrastructure and pipelines from the ground up, not just consuming existing data
• Strong proficiency in Python and SQL; comfortable working across the full analytical stack
• Experience with anomaly detection, predictive modelling, graph analytics, or experimentation frameworks
• Able to work autonomously across multiple business domains and communicate findings clearly to non-technical stakeholders
• Familiarity with AML/CFT concepts, PDPA, or regulated environments is a strong plus