Data Analytics Manager Resume

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Data Analytics Manager Resume Example

Data Analytics Manager


I am a Data Analytics Manager with 7+ years of experience in leveraging data analytics to develop and deploy end-to-end fraud strategies in multiple detection systems targeting different types of fraud and payment channels/products. My career objective is to build a fulfilling career and working with a team of innovative and data-driven professionals committed to driving meaningful business impact.



Manager, Forensic Data Analytics
You and Young – Singapore

  • Manage and drive direction from strategy to execution of applying data analytics which includes data scoping, analysis, visualization and delivering actionable insights in the following forensics engagements:
    • Fraud detection: Design analytics database integrating large volumes of data from disparate systems to build holistic views of customer/transaction profiles used for risk scoring to detect anomalies and flag suspicious transactions for review
    • Investigation and disputes: Apply analytics techniques to examine financial data via transaction lookback analysis, fund movement analysis and fuzzy matching to uncover suspicious behavior and hidden relationships that signal conflict of interest, fraud and non-compliance
    • Compliance and Audit: Run anomaly testing against payments, deposits and audit logs to identify fraud schemes to circumvent existing internal controls or management to override controls and identify gaps in protocols that can be avenues for fraud exploitation
  • Project management and maintaining good client relationships by addressing client needs, managing resource requirements, project workflows and milestones, budgets, billing and   expansion of business opportunities through business proposals and client pursuits
  • Supervise, lead and train junior and senior staffs in multiple engagements and conduct career counseling

Senior Associate, Forensic Data Analytics
Ernst and Young – Singapore

  • Execute end to end analytics in support of forensics engagement – extracting, cleaning, standardization and analyzing voluminous transactional data such as banking, general ledgers, sales, inventory, emails and audit logs to provide new insights, supporting evidence and efficiency gains in fraud detection, compliance, internal audit and investigation engagements
  • Integrate and standardize customer/transactional data in visualization dashboards to provide more visibility of risk exposure, look patterns across different segments and detect outliers
  • Utilize data mining tools to apply different inventory cost layering methods to reconstruct and validate average cost and sales pricing of final goods sold with negative margins to clients which led to leaked revenues or losses
  • Perform inventory movement analysis against 5 years of transaction data (purchase orders, sales, returns) extracted from legacy and new Oracle systems to investigate discrepancies and suspicious inventory losses due to damage or unexplained shrinkage
  • Conduct funds flow analysis by tracing payments made to people of interests and suspicious expense accounts that led to further investigation of inappropriate spending of company funds

Associate, Financial Crime Analytics
Standard Chartered Bank – Hong Kong

  • Perform data mining and analytics in developing, validating and deploying client risk scoring models for Client Risk Assessment (CRA) to help the bank risk rate clients and mitigate money laundering/terrorist financing (ML/TF) risks
  • Map, normalize and integrate client due diligence and transactions across different countries and platforms to perform client, country segmentations and transaction threshold setting to be utilized in client risk assessment models
  • Utilize data mining tools likes SAS to process large volumes of data and perform statistical techniques and apply risk scoring computations in a scalable, reproducible and automated manner

Senior Analyst, Fraud Analytics
NY Bank – New York

  • Collaborate with Product, Operations and Technology teams to discuss emerging fraud trends, formulate and align fraud risk strategies and discuss opportunities for improvement in fraud policies and key metrics with the focus on minimizing fraud losses and maintaining positive customer experience
  • Leverage data mining tools to develop, deploy and monitor real-time and non-real time fraud risk strategies across different platforms for credit and debit card portfolios
  • Develop fraud strategies using decision trees to apply customer segmentations and transaction thresholds against risk profiles, FICO and VISA risk scores
  • Perform merchant profiling and root-cause analysis to detect common point of compromise and collusive merchants for emerging fraud attacks in the ATM and online/keyed channels
  • Optimize overall fraud rule performance to maintain high detection rate and low false positive by proactively identifying emerging fraud trends and gaps through data driven analysis and collaboration with operations team


Master of Arts, Economics
Columbia University

Bachelor of Arts, Economics
University of California – Berkeley Campus

Bachelor of Science, Information Technology
University of Pennsylvania


  • English
  • French
  • German