AML Data Scientist

About GoTyme

GoTyme is a joint venture between the Gokongwei Group, one of the biggest conglomerates in the Philippines, and the Singapore-headquartered digital banking group Tyme. This venture combines the trusted Gokongwei brand, customer base, and distribution ecosystem with Tyme’s globally proven digital banking technology and hands-on experience building South Africa’s leading digital bank, TymeBank, one of the fastest-growing digital banks in the world today.

At GoTyme, we have embarked on a journey to democratize financial services and bring next-level banking to the Philippines. We seek individuals who share our belief that the game is worth changing, to join our growing team of GoTymers as we build, launch, and scale a bank that empowers all Filipinos to navigate a path to financial freedom.

About the Role

We are seeking a highly analytical and technically proficient Data Scientist to enhance our Anti-Money Laundering (AML) program, with a specific focus on Know Your Customer (KYC) and Transaction Monitoring (TM) operations. The successful candidate will leverage data science techniques, machine learning, and statistical modeling to detect, prevent, and investigate financial crime, ensuring the bank's compliance with regulatory requirements. This role involves developing and optimizing AML models, identifying suspicious patterns, and collaborating with compliance, risk, and operations teams to implement data-driven solutions.

• Build, test, and productionize machine-learning and statistical models for KYC risk scoring, sanctions/PEP screening, and transaction-monitoring alerts.


• Mine large, multi-source datasets (payments, behavioral logs, external watchlists) to surface risk indicators and create novel features that improve detection accuracy.


• Partner with AML officers to calibrate rule thresholds; run back-tests and what-if analyses to balance hit-rate, false-positive, and operational-capacity targets.


• Produce clear model documentation, validation results, and audit trails that meet BSP, FATF, and internal model-risk-management requirements.


• Work closely with Compliance, Engineering, and Product teams to embed analytics into customer onboarding flows and case-management systems.


• Track emerging typologies, fraud trends, and regulatory guidance; experiment with graph analytics, anomaly detection, and Generative AI to enhance our controls.

Must Haves

• Bachelor’s or Master’s in Data Science, Statistics, Computer Science, Finance, or related field.

• 3+ years of hands-on data-science experience, ideally in fintech, banking, or financial-crime domains.

• Proficiency in  SQL, Python, Scala; familiarity with big-data frameworks (Spark, Hive, Hadoop) and cloud platforms (AWS, GCP, or Azure). 

• Solid understanding of AML/KYC regulations, red-flag indicators, and transaction-monitoring concepts.

• Demonstrated ability to translate regulatory or policy requirements into analytical solutions and to explain model outcomes to non-technical audiences.

• Excellent problem-solving skills, attention to detail, and written/verbal communication.