Hao (Artin) Ren

** **** ** *, ****** City, NJ ***10

adjdki@r.postjobfree.com

518-***-****

PROFESSIONAL EXPERIENCE:

Usumoya Sushi Omakase Restaurant, New York, New York April 2019 – Present Co-Founder

Working with the James Beard Foundation on the sustainable seafood program Smart Catch;

Managing & maintaining quality of food & guest experience; Promoting the restaurant through PR firms & SNS. JPMorgan Chase & Co., New York, New York August 2017 – March 2019 Project Management Analyst, Firm-wide Risk & Control Project, Regulatory Operations

Managed and maintained the project databases, which cover information from all Lines of Business and Corporate Functions under JPMorgan Chase;

Analyzed data from the databases to generate periodic and ad-hoc reports, develop KPIs, identify trends, etc.;

Coordinated and managed relationships with internal partners in other segments in support of the program;

Reconciled notes, assessments, evidences and databases in response to the internal Quality Assurance;

Helped resolve issues, such as remediating and documenting procedure gaps, across workstreams. Bank of China, New York, New York May – August 2017 KYC/AML Analyst, Risk & Control

Conducted continuous transaction monitoring and case alerts investigation based on the analysis of Suspicious Activities Reports (SARs) and structured transaction pattern in accordance with BSA/AML requirements;

Reviewed procedures with enhanced due diligence on merchant onboarding and transaction monitoring;

Quantified BSA/AML risk assessment metrics and implemented risk mitigation methods and remediation plans. Hana Bank, Shanghai, China May – July 2016

Intern Analyst, Compliance

Used statistics and analytical tools like R, Excel, and Tableau, to perform in-depth analyses of large datasets to identify activities that require compliance investigation;

Performed analyses to provide ongoing and automated monitoring of key risks regarding laws and regulations. S&P Global, Beijing, China November 2014 – February 2015 Intern Risk Analyst, Market Intelligence

Conducted Non-Performing Loans ratings and corporate internal credit ratings, by analyzing qualitative and quantitative criteria (such as risk profile, credit profile, and anchor) defined in S&P’s Rating Methodology;

Assisted in building up client risk scorecard for major securities companies, investment banks and internet corporations in Greater China Region;

Developed new credit risk strategies for client companies to mitigate losses, grow revenue, and get higher rating. JLL, Chengdu, China August – October 2014

Intern, Commercial Real Estate

Developed pricing models of land & space, and market & investment risk prediction using statistical algorithms;

Completed and presented before clients shopping center and amusement park development plans, and the analysis of West China market; Hosted daily meetings with team members and kept project notes and updates. EDUCATION:

Rensselaer Polytechnic Institute, Troy, New York December 2016

Master of Science in Business Analytics

Cumulative GPA: 3.90/4.00

Southwestern University of Finance & Economics, Chengdu, China June 2015

Bachelor of Science in Economics

Cumulative GPA: 87.81/100

PROJECTS & ACTIVITIES:

Interactive Application for Database Systems Course Project October – December 2016

Created an interface that can perform several types of SQL queries, which allows users to explore the data. Data Analytics for Airlines Segmentation & Topic Modeling April – May 2016

Applied several segmentation techniques in R to analyze the flight rating datasets from Skytrax, such as Hierarchical Clustering, K-Means and DBSCAN, and measured performance by Silhouette Coefficient width;

Conducted topic modeling over airline reviews to identify areas for improvement and recommended relevant strategies for underperforming clusters.

Customer Segmentation & Market Basket Analysis Project, f.y.e. Company April – May 2016

Performed members’ analysis including association rules, segmentation, demographics, and purchasing cycles. Business Analytics Capstone Project, Purchase Likelihood Prediction January – May 2016

Cleaned data by fuzzy matching, stemming, duplicates & punctuation removal, missing values imputation, etc.;

Applied predictive techniques such as logistic regression, random forest, neural networks, and support vector machine (SVM); Recommended relevant strategies, including customer acquisition and campaign usage.

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