Digital Marketing Machine Learning

Jiarui (Aria) Lin Boston, MA (willing to relocate) 857-***-**** ********@*****.***

Summary of Qualifications

· Data Analytical and Modeling: Proficient in Exploratory Data Analysis, A/B Testing, and Statistical Concepts

(Hypothesis Testing, Time Series, Regression Analysis, Bayesian Inference, ANOVA, Multivariate Analysis). Experienced in Supervised (Regression Analysis, Random Forest, Decision Tree) and Unsupervised Machine Learning (Clustering).

· Data Visualization: Expertise in creating intuitive and interactive dashboards using Tableau, Power BI, IBM Cognos Analytics, Excel, Google Data Studio.

· Digital Marketing: Skilled in SEO, SEM, PPC, Paid Social Advertising, ROI Analysis, Strategic Marketing, Campaign Oversight, Content Creation, Marketing Materials Creation (Canva), and Social Media Management.

· Programing and Software: Advanced in Python (pandas, Scikit-learn, NumPy, matplotlib), R (ggplot2), SQL, SQL Server tools (SSIS, SSAS, SSRS, SSMS), Microsoft Azure, Excel (Pivot table, VBA), and Google Analytics. Education

BOSTON UNIVERSITY GPA: 3.86 MAY. 2024

Master of Science in Applied business analytics (STEM) Boston, MA Graduate Certificate in Global Marketing Management Relevant Coursework: Financial Concepts, Project Management, Digital Marketing, Ecommerce, Marketing Analytics, Data Mining, Enterprise Risk Analytics, Data Science with Python, Machine Learning. UNIVERSITY OF BRITISH COLUMBIA MAY. 2020

Bachelor of Arts in psychology Vancouver, BC Canada Work Experience

WEB ANALYTICS TEACHING ASSISTANT BOSTON UNIVERSITY JAN. 2024 – MAY. 2024

· Graded assignments, organized office hours, and responded to student inquiries to support academic success, focusing on R and SQL for analytics tasks like constructing database for demographic filtering in class of 30+ students.

· Assisted in delivering course on digital & e-commerce analytics, emphasizing SQL database, web analytics, data mining.

· Enhanced classroom engagement and facilitated proficiency in analytical tools, alongside Google Analytics, E- Portfolio setup through collaboration with faculty during in-class exercises. BUSINESS ANALYST & CONSULTANT ALBA INC. APRIL. 2021- JUNE. 2022

· Maintained relevant data and statistics on intakes, requests, and referrals, coordinating projects across teams and tracking progress using interactive dashboards and ERP system.

· Prepared and delivered weekly and monthly reports using Excel, detailing sales trends, referrals, offers, commission, and income. Conducted ad hoc analysis and presented KPIs to advise stakeholders on data-driven decisions.

· Visualized monthly sales, key performance metrics with Tableau, enhancing trend forecasts and contributing to a 11% improvement in sales forecasting accuracy.

MARKETING ANALYST (INTERN) EF EDUCATION FIRST HANGZHOU MAY. 2018 – JULY. 2018

· Coordinated of offline booth setups and provided consulting for 100+ families, strategically positioned within 3 km of schools and high-traffic areas, using data analysis to target densely populated demographics.

· Utilized linear regression models to forecast customer flows, driving budget and booth placement negotiations, achieving peak visibility and foot traffic.

· Conducted KNN-driven segmentation for marketing materials distribution, engaging potential clients, and acquiring leads & customer information for strategic pricing and targeted marketing. Academic Project

FELINE BEHAVIOUR AND PERSONALITY TRAITS ANALYSIS PYTHON JAN. 2024 – MAY. 2024

· Analyzed behavior and personality traits of 4,300 cats across 56 breeds to identify suitable breeds for potential owners.

· Applied K-means clustering to categorize breeds into 3 clusters based on behavior traits. Conducted hypothesis testing using Pearson correlation and t-tests to investigate relationships between different traits.

· Developed classification models (Logistic Regression, KNN, LDA, SVM, Decision Tree, Naive Bayes) to predict problematic behaviors, achieving 65% accuracy with Logistic Regression. Used Random Forest classifier to predict suitability for first-time owners, achieving 65% accuracy.

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