PhD in Statistics, Rutgers, the State University of New Jersey

• Senior Manager – AML Model Risk Governance & Optimization                                                 05/2017 – Present
  • Developed various statistical methodologies for AML ATL and BTL tuning, provided primary quantitative analytical support to the Model Risk Governance (MRG) and Optimization team to develop, maintain and oversee an effective model risk governance and optimization program that meets regulatory requirements.
  • Using regulatory guidance and the latest compliance analytics techniques, provided primary analytical recommendations and support for the deployment of repeatable, global model for AML surveillance, implementing an industry leading solution.
  • Responsible for overseeing data analytics projects and quantitative analysts
  • Applied statistical methods to organize, analyze and interpret data related to AML. Ensure appropriate methodologies are developed that improve the effectiveness of AML coverage.
  • Developed recommendations for model development and implementation related to AML segmentation, initial threshold setting, tuning and optimization, alert risk scoring and below-the-line sampling methodologies.
  • Collaborate with other AML groups in the identification and assessments of AML risk typologies
  • Ensure data quality, including soundness and completeness, for the design and implementation of methods in SAS databases.
  • Developed metrics and historical alert information for AML transaction monitoring threshold setting. Determine and implement enterprise-wide segmentation, threshold setting and optimization strategy. Explore new approaches and models to improve evaluation of quantitative and qualitative data, and AML transaction monitoring.
  • Managed Model Risk Governance team junior analysts

• • Global AML Insights & Analytics, RBC Royal Bank, Toronto, ON
  • Senior Analyst – AML Analytics                                                                                                           11/2014 – 05/2017
    • Participate and defend TD’s AML model suite with regulators (OCC/CRAD) in the US
    • Review and revise current AML scenario review methodologies; research on new AML methodologies (e.g. multivariate analysis, clustering, etc.)
    • Perform skilled analytical research, analysis, and risk assessment activities and be viewed as a subject matter expert for this area of specialization. Provide feedback and recommendations to improve the quality and effectiveness of the AML testing program. Review the AML Analytics Standards & Guidelines and research on statistical techniques for AML scenario parameter tuning procedures.
    • Proposed new methodologies that improved the AML scenario parameter tuning process, including above-the-line and below-the-line analysis. Recommended numerical techniques for sampling process in the below-the-line analysis. Participated and contributed significantly in the revision of the current AML Analytics Standards & Guidelines. Wrote papers on potential improvements in the AML analytics.
    • Lead team on exploration of data mining methodologies, including logistic regression, support vector machine, random forest, xgboost and bayesian networks, etc, for prioritization of AML scenario alerts.
  • Global Anti-Money Laundering, TD Bank Group, Toronto, ON
  • Statistician/Analytic Consultant                                                                                           03/2014 – 10/2014
    GDIA, Dun & Bradstreet, Short Hills, NJ
  • Capital Market Modeling, Text & Social Media Mining, Marketing Composite Segmentation using SAS EG, SAS EM, R, Angoss KnowledgeSEEKER and KnowledgeREADER.

• Intern                                                                                                                                            05/2011 – 08/2011

• Global Statistics Group, Kraft Foods, Whippany, NJ

• Worked on projects about quality assurance of FTIR spectral data and Advantage Mint Oil analysis.
  • Revised the existing R scripts for quality assurance of FTIR spectral data.
  • Added additional features for quality analysis, such as Confidence Ellipse for score plot, DModX measure (distance to the model in X space), p-value calculation, etc.
  • Developed a standalone user-friendly interface for the quality assurance of FTIR spectral data.
  • Implemented partial quality analysis tools in LIMS. Additional features are under development.
  • Investigated possibility of application of both the standalone interface and the LIMS interface to other types of data.
  • Developed PLS regression model for connection of Advantage Mint Oil data (GC-MS) and Sensory data.

• Intern – Biostatistics                                                                                                                   07/2009 – 08/2009

Dept. of Non-Clinical Statistics, Johnson & Johnson PRD, Raritan, NJ

• Worked on a project related to the study of the stability properties of a pharmaceutical compound.
  • Based on ICH mandated regulatory models, implemented mixed models and the Arrhenius equation on the stability studies.
  • Developed both the linearized Arrhenius model as well as the non-linear parameterization, and applied it to a set of simulated data from both a frequentist as well as Bayesian approach.

• Statistical Consultant                                                                                      Spring 2011, Spring 2009, Fall 2010
  Dept. of Statistics, Rutgers University, Piscataway, NJ
  • Worked as Statistical Consultant for Office of Statistical Consulting (OSC), job duties including:

• offer helps on Faculty/researcher/student research projects, including statistical methodology, experimental design, data collection and analysis, and interpretation of results
• Inter-disciplinary collaboration and methodology development
• Grant planning and development, power analysis, proposal preparation
• Graduate student education and training in quantitative analysis skills
• Statistical advice and statistical analyses of data for clients
• Tutorials on use of statistical software, technical support, and instruction for supported software
• Extracting and/or converting data into a format suitable for analysis
• Short courses on statistical topics of current interest

• R