Data Analytics - Marketing Insights - Python & SQL

Tiancheng Guo

+1-217-***-**** *******@*******.*** San Francisco, CA, USA linkedin.com/in/tiancheng-guo-197b3b21a/ EDUCATION EXPERIENCE

University of California, Berkeley Berkeley, CA

Master of Analytics August 2025 - August 2026

• Relevant coursework: Analysis and Design of Databases, Optimization Analytics, Data Modeling, Statistics, and System Simulation, Machine Learning and Data Analytics, Risk Modeling, Simulation, and Data Analysis, Data Structures and Algorithms University of Illinois Urbana-Champaign Champaign, IL Bachelor of Science in Statistic, Econometrics (Double Major) August 2021 – May 2025

• Dean's list: Fall 2021, Spring 2022, Fall 2022, Spring 2023, Spring 2024, Fall 2024

• Relevant Coursework: Regression Analysis, Time Series Analysis, Unsupervised Learning, Applied Machine Learning, Statistical modeling PROFESSIONAL EXPERIENCE

INNOVA AI TECH, LLC Remote, US

Data Analyst Intern June 2025 - September 2025

• Performed customer segmentation by applying RFM analysis and K-means clustering on 100K+ transaction records, uncovering distinct behavioral cohorts that informed targeted marketing campaigns, ultimately driving a 12% increase in retention rates

• Designed, executed, and analyzed A/B testing frameworks using Python (scipy, statsmodels) and conducted statistical testing (t-test, regression), enabling leadership to reallocate budgets effectively and realize an additional $1.5M in annual ROI

• Automated segmentation and analysis workflows leveraging Python and SQL, reducing repetitive manual processes by 30+ hours per quarter and collaborating cross-functionally with marketing teams to launch campaigns 25% faster with improved accuracy

• Built and deployed interactive Tableau dashboards, Amazon QuickSight, and Excel to visualize RFM segments, clustering outputs, and A/B testing results, enabling executives to track KPIs in real time and align budget decisions with customer value insights Ehomie Chicago Chicago, IL

Marketing & Sales Intern February 2025 – May 2025

• Increased sales conversion by 17% by analyzing Customer, Apartment, and Order databases through Excel, SQL, and Python, applying data modeling and regression to identify key pricing and location-based behavioral patterns, which drove evidence-based marketing and pricing BI decisions

• Enhanced promotion forecasting accuracy by 22% by developing Python ETL pipelines to standardize pricing data, performing time-series analysis, and integrating automated outputs into Tableau dashboards that optimized listing-level promotions and pricing strategies

• Improved customer retention by 9% and social engagement by 22% by leveraging Python scripts to process Rednote post-performance data, and generate actionable BI insights such as identifying high-impact content drivers that informed targeted campaigns and engagement initiatives

• Automated Excel report construction that combined SQL data extraction, Python preprocessing, and Tableau visualization to unify marketing data sources, enhancing information management efficiency, reducing manual reporting time by 15+ hours per month and improving stakeholder visibility into KPI trends

Bank of Nanjing Jiangsu, China

Data Analyst Intern June 2024 - July 2024

• Improved fraud detection accuracy by 19% by analyzing 250+ transaction records with Python, engineering behavioral features, and applying logistic regression and random forest classifiers to refine real-time anomaly detection

• Reduced false positives by 14% through implementing SMOTE oversampling, feature scaling, and hyperparameter tuning, which ensured balanced datasets and optimized fraud prediction performance, leading to fewer disruptions for legitimate customers

• Accelerated model training time by 25% by developing automated preprocessing pipelines in Python, including or data cleaning, encoding, and transformation, enabling faster iteration cycles and more efficient model deployment

• Delivered actionable business insights by building interactive Power BI and MicroStrategy dashboards to visualize fraud patterns by region, merchant category, and time of transaction, empowering financial risk teams to proactively prevent fraud and minimize losses PROJECT EXPERIENCE

Chicago Divvy Bike Share Business Analytics Chicago, IL Individual Project August 2024 - December 2024

• Built a scalable data pipeline and ETL framework to preprocess 5M+ bikeshare records using Google BigQuery SQL and Python, performing missing value treatment, feature engineering, and data transformations that enabled accurate downstream analytics and faster business insight discovery

• Enhanced operational visibility by developing interactive Power BI dashboards and Amazon QuickSight dashboards, uncovering user demographics, trip frequency, and platform utilization trends that directly informed marketing strategies and operational planning

• Identified two major operational inefficiencies-imbalanced checkout-return station flows and seasonal demand fluctuations-through time-series regression, and automated SQL-based aggregation queries and validation checks to improve data accuracy and optimizing pipeline refresh cycles

• Proposed actionable, data-driven solutions, including a machine learning demand forecasting model and route-based promotional strategies, offering opportunities to reduce idle inventory costs, improve resource allocation, and drive measurable increases in platform revenue Beer Profiles and Rating Projects Champaign, IL

Individual Research August 2024 - December 2024

• Developed a data-driven recommendation beer system by applying K-means and hierarchical clustering algorithms in Python on customer beer rating datasets, generating 6 distinct taste-based clusters that enabled breweries to design targeted marketing campaigns and product positioning strategies

• Enhanced cluster validity and interpretability by using t-SNE dimensionality reduction and silhouette score analysis, visualizing consumer preference groups in Tableau dashboards, which provided breweries with actionable insights to refine product offerings and increase sales performance ADDITIONAL INFORMATION

Languages & Tools: Python (pandas, numpy, scikit-learn), SQL, R, Excel, Tableau, Power BI, QuickSight, MicroStrategy Data & BI Skills: ETL Pipeline Development, Information Management, Data Modeling (Star Schema), Statistical Testing (t-test, regression), Data Visualization, Automation, Troubleshooting

Core Strengths: Analytical Skills, Analytical Thinking, Problem Solving, Ownership, Communication, Data-Driven Decision Making, Working Under Pressure

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