RODRICK MPOFU
Entry-Level Data Scientist
Canton
Summary
Accomplished data scientist with a proven track record in data analysis and problem-solving. Developed innovative models that significantly improved decision-making processes. Highly collaborative team player, adaptable to dynamic project requirements. Skilled in Python, SQL, R, and machine learning algorithms.
Overview
3
3
years of professional experience
4
4
years of post-secondary education
Work History
Student Technician
St. Lawrence University
Canton, NY
06.2022 - Current
- Resolve software, hardware, and connectivity issues for 3,000+ students, faculty, and staff
- Liaison for the Bloomberg terminal, ensuring proper use across departments
- Administered in-person and virtual technical support to 3,000+ customers
Data Science Intern
LinkedIn Corporation
Sunnyvale, CA
05.2024 - 08.2024
- Developed actionable recommendations using machine learning models (XGBoost and SHAP) to enhance campaign performance and cost efficiency by optimizing targeting facets
- Conducted extensive analysis of LinkedIn advertising campaigns, categorizing targeting facets by usage frequency and impact on Cost-Per-Click (CPC) and total clicks metrics
- Built a new dataset from historical campaign data, extracted relevant targeting features, and cleaned data to ensure accuracy of machine learning models
- Trained XGBoost model to predict campaign outcomes, achieving a 48% improvement in model accuracy over baseline, and utilized SHAP values for interpreting model predictions
- Generated rules and recommendations using SHAP and defragTrees to guide advertisers in improving campaign targeting, leading to potential reductions in CPC
- Cooperated with product managers, data scientists, and engineers to refine targeting strategies and ensure practical implementation of recommendations within LinkedIn's platform
Data Engineering Intern
The New York Times Corporation
New York City, NY
06.2023 - 08.2023
- Boosted efficiency, reducing test setup time by 40%, of A/B testing process by developing an automated pre-run calculator, streamlining decision-making process
- Enhanced predictability in A/B test planning by utilizing historical data to pre-compute baseline conversion rates, allowing teams to make data-informed decisions on sample sizes and estimate experiment run times accurately
- Reduced manual analysis time by implementing data transformation pipeline to periodically computes conversion rates and sample sizes from historical experiment data
- Accelerated data retrieval times by integrating Google's Firestore DB as a key-value store, ensuring essential metrics were quickly and efficiently accessible by teams
Education
Bachelor of Science - Data Science
St. Lawrence University
Canton, NY 08.2021 - 05.2025
Skills
Java Programming
SQL
Pandas
NumPy
Matplotlib
ggplot2
Tableau
Logistic Regression
Clustering
Principal Component Analysis (PCA)
Scikit-Learn
XGboost
Random Forests
Decision Trees
SHAP (SHapley Additive exPlanations)
BigQuery
Google Cloud Platforms
Significant Projects
Analyzed cryptocurrency market price data including Bitcoin, Ethereum, and Litecoin, over six years., Explored price correlations, trends, and volatility through daily Python analysis.
Activities
Colorstack
Timeline
Data Science Intern
LinkedIn Corporation
05.2024 - 08.2024
Data Engineering Intern
The New York Times Corporation
06.2023 - 08.2023
Student Technician
St. Lawrence University
06.2022 - Current
Bachelor of Science - Data Science
St. Lawrence University
08.2021 - 05.2025