Charles Qihang Zhou Email & Phone Number

New York, Ny, Us

New York, Ny, Us

1. Project Management: Initiated and led the Customer Lifetime Value (CLV) project, introducing a novel approach to evaluating wealth clients based on AUM and growth potential. Developed a Python-based lookalike model to quantify CLV, collaborating closely with the COO and presenting to senior management. Successfully drove project adoption, significantly enhancing field sales efficiency through optimized client time allocation.2. Predictive Machine Learning: Collaborated with the COO to identify key success drivers in sales attempts. Created a Python-based machine learning pipeline to generate actionable insights for engaging hard-to-reach advisors, increasing the break-through rate by 3x.3. A/B Testing: Spearheaded a team of 3 analysts to conduct BlackRock’s first A/B test experiment in Python. This pivotal project has influenced future campaign prioritization by illustrating the impact of past campaigns on client engagement and net new flows.4. Natural Language Processing: Directed two summer interns in building a Python-based search engine for filtering Dow Jones news. This tool significantly aided the Cash Sales team in identifying prospects with recent financial activities indicating potential large cash inflows.5. Data Visualization: Developed dashboards in Python and Tableau for the COO, enabling effective monitoring of sales effort allocation and net new fund flows within BlackRock's iShares business.6. Customer Segmentation: Utilized Python to analyze multiple data sets, successfully segmenting over 500,000 US financial advisors into four distinct client profiles. This strategic initiative provided the sales team with data-driven insights, enabling more effective management of client relationships.

Austin, Texas, Us

1. Data-Driven Story Telling: Leveraged alternative data in Python to assess the efficacy of Oracle-modeled audience targeting, aiding the sales team in crafting compelling marketing narratives and collateral.2. Automation: Engineered a Python-based automation pipeline integrating JIRA API to streamline the extraction and cleansing of ad viewability data. This innovation liberated two full-time employees from manual processes, significantly optimizing workforce efficiency.

Austin, Texas, Us

Graduated from a 2-year Rotational Analytics Program with exposure to 4 different roles in data science.1st Rotation – Integrated Analytics Solutions (Aug 2016 – Feb 2017)• Web Data Collection: Automated the process to collect the brand penetration rate from the website of IRI, a market research company, by searching the most similar products in Python, which reduced the touch-time by 99%.• Data Wrangling: Implemented an end-to-end pipeline to deliver buy-through-rate campaign reporting by integrating large datasets from multiple sources using Hive, Python and Redshift.2nd Rotation – Strategic Analytics (Feb – Aug 2017)• Data Visualization: Developed a web-based dashboard from scratch to track the growth of qualified marketing leads using Python Flask, AngularJS and C3.js, which has enabled the marketing department to make quicker decisions on marketing efforts.• Ad Hoc Support: Provided internal and external data-driven product support to various stakeholders in quick turnaround mostly by using SQL and Python.3rd Rotation – Identity Science (Aug 2017 – Feb 2018)• Big Data Pipeline: Designed and implemented a scalable data pipeline in AWS using PySpark, Python, Luigi, and Docker. This system established key metrics for assessing the accuracy of cookie-to-household connections.• Big Data Analysis: Delivered data-driven reports that connected over 2 billion online impressions and 400 million mobile devices to offline households using PySpark and Hive, which provided visibility into match rate and cross-device coverage of ODC ID Graph with new media partners.4th Rotation – Applied Research (Feb – August 2018)• ML Model Validation: Overhauled the existing validation methodology with a new representative U.S. population dataset, addressing data skewness and enhancing model performance evaluation accuracy.

Setagaya-Ku, Tokyo, Jp

1. Market Basket Analysis: Utilized PySpark to identify groups of products that are negatively associated from over 1 million user transactions, improving the efficiency of smart coupon system.2. Text Analytics: Analyzed a dataset of wine product titles in Python and generated a dictionary of Japanese-English synonyms, enabling the team to improve the product-matching system.

Santa Clara, Ca, Us

Worked as the Business Intelligence Analyst for GMYLE (Gadgets Make Your Life Easier), an E-Commerce retailer developing consumer electronics, gadgets and accessories through multiple online platforms.1. Web Data Collection: Developed a web scraping program in Java to collect product ranking information and customers'​ review on Amazon's website, saving 15 work hours per week.2. Business Intelligence Analyticsi) Created an index to measure the market share of the company's products on Amazon by using log-linear model in R to analyze the correlation between products'​ ranking and advertising impression.ii) Applied the index to create daily reports in MS Access to monitor market share change by wrangling data from inventory, sales and product MySQL databases.iii) Developed an alerting system in MS Access to detect recent bad reviews from customers.

Master’S Degree, Management Information Systems

Bachelor’S Degree, Economics

Exchange Program, Economics