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Sinong Ou

Sinong Ou

Senior Quantitative Modeling Manager

Regions Financial

Technologies

My Portfolio Highlights

My New Course

Artificial Intelligence (AI) Concepts in Python

My New Workbook

Course Notes: Credit Risk Modeling in Python

My New Workbook

Find and Visualize clusters with K-Means

My New Course

Artificial Intelligence (AI) Concepts in Python

Analytical daredevil, fearlessly diving into the abyss of data complexity.

My Work

Take a look at my latest work.

course

Artificial Intelligence (AI) Concepts in Python

DataLab

Course Notes: Credit Risk Modeling in Python

DataLab

Find and Visualize clusters with K-Means

course

Artificial Intelligence (AI) Concepts in Python

My Certifications

These are the industry credentials that I’ve earned.

Other Certificates

Garp Financial Risk Manager

CFA CFA

Scrum Professional Scrum Master II

DataCamp Course Completion

Take a look at all the courses I’ve completed on DataCamp.

Courses

Object-Oriented Programming in Python
Artificial Intelligence (AI) Concepts in Python
Credit Risk Modeling in Python
Introduction to Portfolio Risk Management in Python

My Work Experience

Where I've interned and worked during my career.

Regions Bank | Sep 2022 - Present

Senior Quantitative Modeling Manager

- Manage the financial strategy and modeling team. - Hands-on modeling and data skills using Excel, Python, SQL, etc. - RAROC, IRR, ROA, RORC, EC, EL, CPR - ESG solar, geothermal energy, etc. - ABS capital market framework
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Regions Bank | Oct 2021 - Sep 2022

Senior Quantitative Analyst

- Financial Strategic Initiatives: studied for market verticals, strategic partnership, OpEx analysis, competitive intelligence, etc. - Acquisitions: assisted with data cleaning and valuation analysis - Capital Market Financial Risk Hedging: evaluated, analyzed, and executed interest rate swaps (IRS) with PNC Bank - Predictive Analysis: applied data science techniques and machine learning models for KBRA, Macroeconomic Indices, etc. - Fixed-income Product Creation: created various loan products based on the interest rate, loss rate, weighted average life, mortality rate, etc., by comparing the incurred credit loss model and the current expected credit loss model, to determine net yield - Consumer Loan Tier Pricing: priced loan by different tiers based on FICO score, debt-income ratio, risk profile, credit history, and home equity - Quantitative Credit Risk Analysis: quantified prepayment risk, charge-off rate, historical loan balance, and other risk factors - Model Validation: evaluated accuracy of internal and external models for different purposes and consolidated info in slides

Arnold's Office Furniture | Aug 2020 - Apr 2022

Consultant - Information Technology Business

- Cloud Software Integration: integrated systems between Salesforce, Woo Commerce, Word Press, QuickBase, via gravity form or API call using Zapier or WebHook - Automation Solution: developed solution for automated processed for new customer, order, or trigger by time. via Zapier or the automation tools in QuickBase - ERP Platform Development and Support: built and support the operational efficiency, integration, and information visibility Cloud Software Integration and Automation -Salesforce -Woo Commerce -QuickBase -Zapier -WordPress ERP Platform Development and Support -QuickBase

Bank of America | May 2021 - Oct 2021

Quantitative Application Developer - Contractor/Consultant

- Data Testing: used queries to check the data quality of SQL, Oracle, and Teradata - Data Profiling: profiled data in Python using Pandas Data Profile library with customized periodical data quality tracking script - Outlier Detection and Analysis: applied machine learning techniques, such as the Local Outlier Factor and K-Modes Clustering to profile numerical and categorical data to identify outliers based on the historical data pattern - Solution Delivery from Backend to Frontend (Python, Linux, JSON): developed all solutions in Python and tested in the Linux environment, then deployed in the front-end application for the data testing group - Technical Writing for Documentation: wrote documents for different processes of data profiling interactive with data testing, which was defined and established by me, that provides a thorough technical review and a step-by- step guidance

Northwestern University | Nov 2020 - Jul 2021

Instructional Assistant

Financial Fundamentals [Time-Series Analysis, Financial Ratios, Financial Analysis, Financial Modeling] Machine Learning Applications in Finance [Algorithmic Trading, Random Forests, k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Linear Regression, Scikit-learn, Forecasting, Logistic Regression, Deep Learning, Recurrent Neural Networks, TensorFlow, Keras, AWS SageMaker] Programming and Financial Libraries [Python, Pandas, PyViz, APIs, Amazon Web Services, SQL, Numpy, SciPy] Blockchain and Cryptocurrency [Solidity, Ethereum, Smart Contracts, Consensus Algorithms, Transactions, Validation, Distribution Ledger, Cryptocurrency, Truffle Suite, Ganache]

LQD Business Finance | Nov 2019 - May 2021

Quantitative Analytics Associate

Quantitative analysis tools - Python, SQL, Excel, VBA, R, Tableau, Power BI Model - 1. Credit risk model: delinquent rate prediction using machine learning model fbprophet 2. Cash flow liquidity risk predictive model: predict the business performance of borrowers based on third-party data provider of the business' bank transactions to predict the financial health of the company 3. Macroeconomic indices prediction: predicted Russell 2000, S&P, GDP by state by industry so the market risk can be predicted bottom-up from a more granular perspective. Used the XGBoosting model Solution - 1. Delivered OCR solution for scanning and automating the data entry of tax returns via Abbyy OCR and UiPath. 2. Used QuickBase to build SAP/ERP system for borrowers and our internal system so the operations can be done on a centralized and traceable platform. We sold our services to borrowers for annual subscriptions revenue. 3. Developed SaaS of Commercial Lending via Mendix, with our internal centralized and automated commercial lending platform as a blueprint, I have led five teammates to transform the solution to SaaS and delivered the UI before I left the company. The expertise of Modeling: credit risk, macroeconomics indices, and cash flow predictions via Python, SQL, and Machine Learning The expertise of Solutions: OCR solution development and delivery via Abbyy; integrating RPA solutions via UiPath and BI. Machine learning predictions for macroeconomics, credit, and financial data using methodologies such as time-series analysis, Gradient Boosting (XGBoost), Random Forest, and Reinforcement Learning Reporting and Visualization: Power BI, Tableau Product Manager and Developer: developed an ERP System based on QuickBase, sold to customers with options of a monthly subscription. The expertise of Software Development: ERP platform via QuickBase; and SaaS platform via Mendix Quantitative analysis via tools: Python, SQL, Excel

Becker Logistics, LLC | Jan 2019 - Nov 2019

Financial Data Analyst

Credit risk analysis, Data Analysis, Data Management; Robotic Automation Processing, Process Analyst, Intelligent Automation; Financial data analysis, budgeting analysis, predictive analysis; Kofax RPA and OCR development; Operational Support(Customer setup, credit check, and Software Maintenance); IT Support(Auto rating, API, and EDI); Building different models and training other office managers with new models. Software/tech skills: SQL Server, Power BI, Excel, Tableau, and Robotic Automation Process Summary - At Becker, I was the quality control for data quality (financial and operational), reporting, underwriting, business strategy, and partnership. Credit Risk Analysis: evaluated potential customers’ credit history reported by the third-party provider and internal metrics Financial data analysis, budgeting analysis, predictive analysis Market Research and Risk Prediction: market price prediction for gas, oil, the impact of a trade war, and other factors. Quantify the loss that could be incurred in regard to one and multiple factors. Corporate Financial Reporting: organized and put together unstructured financial data for different levels of reports for the office’s manager, COO, and CEO for KPI, employee evaluation, incentive plans, and budgeting Budgeting Prediction: for the whole office data, used time-series analysis to predict the remaining of the year performance and alternate the company monthly goals to achieve the annual goal Robotic Process Automation: helped operations, marketing, and accounting team to automate the daily reluctant process, make an organized report and uploading template and maintain any simultaneous process Operational Support (Customer setup, credit check, and Software Maintenance); IT Support(Auto rating, API and EDI) Kofax RPA and OCR development Robotic Automation Processing, Process Analyst, Intelligent Automation Credit risk analysis, Data Analysis, Data Management

CFA Society Chicago | Mar 2018 - Jan 2019

Research Analyst

N/A

Northern Illinois University | May 2018 - Dec 2018

Investment Research Assistant

Contributed research with credit default swap (CDS) in the Finance Department.

Northern Illinois University | Aug 2016 - May 2018

Math Instructor

As a qualified SI leader, I attend all classes with students and set up two hours supplementary sessions and three hours office hours for students to get after-class help. Compared to a tutor, an SI leader has a fixed group of students with consistent company and specific facilitation. I act as a model student in class as well as track each students' grades, attendances and in- class performance. I usually walk around and help students in formal classes to make sure they all understand everything. Except for in-class efforts, I prepare questions, quizzes and learning methodologies for students. Every week, we have an hour paperwork time which we are supposed to make a plan for current weeks' SI sessions. I am always available to my students, even outside my SI sessions and office hours. I love to get to know them because each individual has an interesting spirit and tons of stories. In addition, the SI program provides both face-to-face training and class observation for us.. In this job, I have learned interpersonal skills, communication skills, being responsible and always being patient and positive. I love to think about how to improve the program and proposed a scheduling change. I collected all SI leaders' sessions and office hours so that students could have more flexibility choosing their schedules. This idea is currently applied and runs very well. Our quote is: Failure is not an option, which I changed a little bit: Success is the best option.

CFA Society Chicago | Aug 2017 - Feb 2018

Research Fellowship

Our team won the third place out of 16 teams in CFA(Certified Financial Association) challenge of the general Chicago area. We analyzed the given company, CDW in terms of its business description, industry overview and competitive positioning, corporate governance, regulations, financial performance, valuation, investment summary and investment risks. I was responsible for corporate governance and investment risks. Besides the report, our team made a PowerPoint to present in front of judges, which was well received. For corporate governance part, I read through CDW and its main competitors' SEC filings and ISS Quality Score reports, and discussed about CDW's corporate governance from the board of directors, shareholder structure, corporate management, executive compensation and social responsibility. One thing that I got inspired when doing investigation is the potential takeover risks by activist investors who has filed 13D forms of other companies. I consider they have the potential to be activist investors for CDW as well. For the investment risks part, I used VaR and ES to evaluate a general financial risk, with consideration of market risks,credit risks, operational risks, business risks, legal risks and key personnel risks. For example, we considered tax reform repealing as a major market risk, where we used DCF model to calculate the next presidential period's free cash flow and find that our DCF price decreased less than $7. At last, we did an oral presentation with 40 slides which we have prepared for two weeks so that we were able to give a perfect, flawless presentation. However, Q&A par somehow got us so that we did not win the title when our report was ranked second.

Northern Illinois University | Jan 2017 - May 2017

Satistics Researcher

Today, the four major sports leagues frequently use statistical models to predict winning percentage of teams and pick up the best players. According to such trend, I did my Honor’s research capstone about predicting the NBA winning percentage based on all team's statistics since the NBA first introduced three-points to the game. In the research, I worked on SAS and used Time Series to identify and modify the model. Results: Before 2015-2016 season, three-points does not weigh much in predicting the winning percentage. Thus, I did not contain three-points in the general model I built. However, in the recent two seasons, players and teams had higher shooting average and shot more three points than before. Therefore, I put three-points into the modified model because that represents the future of the NBA. Comparing two models, I found that teams with higher three points goals fit the modified models better while other teams fit the general model better.

Northern Illinois University | Feb 2016 - May 2017

Board Treasurer

I was the treasurer of the NIU actuarial science club and responsible for managing expenses of the club in activities. Also, I helped to host many guest speaker meetings, information meetings, interview workshops and career coaching meetings. I am always active about new ideas and decisions. For example, NIU does not have many insurance companies so we held an interview with Allstate for any students who wanted to be an actuary. The feedback from both employers and students are awesome and we will keep doing this as we have more and more connections with companies. The board of the club is very good. People are always nice, wise and responsible. We had a good time either in social or school events. In April 2017, I led a team participate in NIU Cares' day to give back to the community representing our club. It was a gorgeous day when seeing everyone worked hard to clean and do yard works for the homeless people shelter. All in all, this club is the best team I am ever involved. My leadership, management skills, promoting skills, interpersonal and communication skills are all improved in the club.

Northern Illinois University | Jan 2016 - Dec 2016

Mathematics Tutor

Tutor provides after-class help for students to master such subjects where I tutored Calculus III, Linear Algebra, Business Statistics and theory of Probability. In this job, most of time I was running to different tutoring centers every evening, of which one night I was hit by a car on campus, so I quit this night- time job. Except for the car accident, this job is excellent in making great connections with students and helping them to achieve their own goals. When students ask you questions, you have to be patient, positive and supportive. Also, if it is a quiz or homework question, you cannot give the answer but a similar question to help them understand. So far, six students I have been tutored or get improve in grades of at least one letter. With a tutor as a monitor beside them, students worked harder and performed better. This job is my first job and I worked for a whole year. If it wasn't the accident, I could work for a longer period.

My Education

Take a look at my formal education

Massachusetts Institute of Technology
Ottawa University
Northern Illinois University
Northern Illinois University
Anhui University of Finance and Economics

About Me

Sinong Ou

Lead the world in data science, FinTech, blockchain, and quantitative research and analysis.

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