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Ding Ma - Data Science

Atlanta, GA
May 28, 2020

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Professional Profile

Technical Skills Profile

Analytic Development: Python, Matlab, R-Programing, SAS, Spark, SQL VBA, C++, Java

Python Packages: Numpy, pandas, scikit-learn, TensorFlow, SciPy, Matplotlib, Seaborn, Numba

IDE: Jupyter, Spyder, MatLab, RStudio, Visual Studio

Version Control: GitHub, Git,

Machine Learning: Time Series Prediction, Natural Language Processing & Understanding, Machine Intelligence, Machine Learning algorithms

Data Query: Azure, Google, Amazon RedShift, Kinesis, EMR; RDBMS, SQL and data warehouse, data lake and various SQL and NoSQL databases.

Deep Learning: Machine perception, Machine Learning algorithms, Neural Networks, TensorFlow, Keras.

Artificial Intelligence: text understanding, customer behavior predictive modeling, classification, pattern recognition, targeting systems, ranking systems.

Analysis Methods: Advanced Data Modeling, Time Series Analysis, Forecasting, Predictive, Statistical, Sentiment, Exploratory, Monte Carlo Simulation, Stochastic Calculus, Bayesian Analysis, Inference, Models, Regression Analysis, Linear models, Multivariate analysis, Sampling methods, Forecasting, Segmentation, Clustering, Sentiment Analysis, Predictive Analytics, Big data and Queries Interpretation, Design and Analysis of Experiments, Association Analysis

Analysis Techniques: Classification and Regression Trees (CART), Support Vector Machine, Random Forest, Gradient Boosting Machine (GBM), TensorFlow, PCA, RNN, Regression, Naïve Bayes

Data Modeling: Bayesian Analysis, Statistical Inference, Predictive Modeling, Stochastic Modeling, Linear Modeling, Behavioral Modeling, Probabilistic Modeling, time-series analysis, survival analysis

Applied Data Science: Natural Language Processing, Machine Learning, Social Analytics, Predictive modeling

Soft Skills: Excellent communication and presentation skills; ability to work well with stakeholders to discern needs accurately, leadership, mentoring, coaching

Professional Experience

American International Group Woodland Hills, CA

Associate Director

Enterprise Risk Management - Machine Learning

October, 2014 - Present

American International Group, Inc., also known as AIG, is an American multinational finance and insurance corporation with operations in more than 80 countries and jurisdictions.

Lead the development of the expected profit projection engine by applying machine learning and data science algorithms with financial engineering, actuarial science.

Lead customer behavior predictive modeling by applying machine learning techniques with survival analysis and financial engineering

Lead the detection and prevention of fraud affecting retirement customers including natural language processing, customized classifiers with TensorFlow framework and work with operational team on business rules and policies

Work with product development team for optimal product design, pricing and marketing strategy

Developing hazard models for credit loss projection by applying Cox Proportional hazard model and logistic probability model with customer cohorts, and explore a boosting model and DNN model

Oversee/Manage daily asset liability modeling production on AWS with Oracle database and other data formats

Hartford Financial Service Group Hartford, CT

Director and Risk Manager

Quantitative Group, Modeling, Analysis & Risk Strategy - Data Scientist

April 2011 – October 2014

The Hartford Financial Services Group, Inc., usually known as The Hartford, is a United States-based investment and insurance company. The Hartford is a Fortune 500 company headquartered in its namesake city of Hartford, Connecticut.

Lead the development of trading strategies by applying data science and machine learning with technical and financial engineering

Lead the development of the expected profit projection engine by applying machine learning with financial engineering, actuarial science.

Lead the development of customer key risk indicator using natural language processing (NLP) technical and LSTM to process text records.

Build various statistical models Statistical algorithms involving Time Series analysis, Survival Analysis, Multivariate Regression, Linear Regression, Logistic Regression and PCA in financial projection

Perform inforce management including survival analysis, churn/retention analysis, risk identification

Design and implement the enterprise Financial Value-at-Risk model

Lead various cross-department projects and worked closely with internal stakeholders such as business teams, product managers, engineering teams

Worked on customer segmentation using an unsupervised learning technique clustering.

ING Group West Chester, PA

Quantitative Associate

Financial/Market Risk Management Department – Data Science

September 2007 – April 2011

The ING Group is a Dutch multinational banking and financial services corporation headquartered in Amsterdam. Its primary businesses are retail banking, direct banking, commercial banking, investment banking, wholesale banking, private banking, asset management, and insurance services.

Support trading group by developing algorithms using machine learning/statistic models with financial engineering technical

Build various statistical models Statistical algorithms involving Time Series analysis, Survival Analysis, Multivariate Regression, Linear Regression, Logistic Regression and PCA in financial projection

Performed fund analysis for Investment Management, improving methodologies for modeling and hedging fund driven exposure, providing monthly fund dashboard, quantifying the risk characteristics of funds

Developed portfolio replicating process for asset-liability portfolio using plain vanilla instruments for Asset-Liability/Economical Capital management

Hartford Financial Service Group Hartford, CT

Quantitative Analyst

Quantitative Group

March 2006 – September 2007

Worked with key mathematicians and statisticians to build financial models and perform statistical analysis

Designed and implemented Integrated Regrouping Information System to generate hedging (GAAP) P&L report with MySql database

Developed models and documented algorithms for production Scenario Generator


Baylor University Waco, TX

Ph. D in Mathematics, December 2005

Specialization: Computational Finance, Numerical Analysis and Mathematical Programming

Louisiana Tech University Ruston, LA

Ph.D. Candidate in Computational Analysis & Modeling, December 2002

Specialization: Computational Biology, Numerical Analysis and Modeling.

Beijing University of Chemical Technology Beijing, China

M.E in Mechanical Engineering, June 2001


CFA Chapter Holder June, 2019

Advanced Risk and Portfolio Management Training

By Prof. Attilio Meucci at Brunch College of CUNY August, 2010

Financial Risk Manager (FRM) April, 2010




15 years of experience

Data Science and Quantitative Modeling


Quantitative Modeling

Machine Learning

Predictive Modeling and Analytics

Market Risk Modeling Credit Risk Modeling

Fraud Prevention

10 years of team lead as director of quantitative modeling

15 years of quantitative modeling, data science, actuarial science

Extensive experience of cross-department project management

Experience in the application of Naïve Bayes, Regression Analysis, Neural Networks/Deep Neural Networks, Support Vector Machines (SVM), and Random Forest machine learning techniques.

Experience in machine learning models, statistical models on big data sets using cloud/cluster computing assets with AWS and Azure.

Extensive experience on time series analysis and survival analysis

Work with product development department for optimal product design, pricing and marketing strategy

Hands on experience in credit risk modeling from hazard models, severity models and exposure at risk models

Extensive quantitative modeling in financial derivatives, asset liability management,

Customer behavior predictive modeling on lapse/churn, withdraw

Fraud detection and prevention with financial transaction

Extensive model validation experience in data science, and quantitative modeling

Excellent communication skills (verbal and written) to communicate with clients/stakeholders and team members.

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