Amelia Farrell

Data Scientist | Business Analyst | Tableau Developer

Driving business growth & bridging gaps through data-driven insights

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About Me

A highly skilled and experienced Data Scientist & Business Analyst with expertise in strategic planning, data analytics / visualization, ML / AI, and project management. With a proven track record of developing and executing strategic initiatives within logistics, manufacturing, and semiconductor industries. I specialize in transforming complex data into actionable insights that drive business growth.

With a cup of Starbucks unlimited coffee, my powers 10x.

Outside of work, I enjoy deep diving whitepapers, personal data/ML projects, hiking, camping, and home DIY projects.

Education

M.S. Data Science

Bellevue University, Bellevue NE, 2025

B.S. Psychology

Fitchburg State University, Fitchburg MA, 2015

Amelia Farrell

Skills & Expertise

Data Analysis & Machine Learning

Python/R/HTML/CSS SQL/NoSQL Excel (VBA, DAX) MATLAB Pandas/NumPy/Scikit-learn/PyTorch/TensorFlow Seaborn NLTK Regression Time Series Association Rules Decision Trees/Gradient Boosting Supervised/Unsupervised Learning Natural language processing (NLP) Generative AI Git Neural Networks Snowflake Apache Spark Automation Solutions

Visualization & BI Tools

Tableau Power BI SAP BusinessObjects Excel Macros Google Analytics KPI Development Executive Dashboards Data Storytelling

Project & Process Management

Clear Communication Stakeholder Management Cross-Functional Collaboration Project Management Risk Management Documentation & Training Jira/Confluence/SharePoint/Salesforce Agile/Lean/Six Sigma SDLC Supply Chain Management Strategic Planning Process Improvement Change Management CRM/ERP Systems

Professional Experience

Data Analyst

On Common Ground - Attleboro, MA

January 2018 - August 2018

  • Design, develop, and implement database & performance measurement (KPI) system to track organizational impact
  • Ran statistical analysis to track program impact and identify unknown variables, relationships, and trends
  • Collaborated directly with program managers and external stakeholders to collect feedback and diagnose program data anomalies

Senior Data Analyst

Infineon Technologies - Leominster, MA

May 2019 - April 2022

  • Lead monthly S&OP process and demand planning
  • Build forecasting models and automation solutions
  • Develop data-driven dashboards for decision making

Strategic Planning Manager (PMO)

Infineon Technologies - Remote

April 2022 - Oct 2024

  • Drive process of balancing headcount across 300+ R&D projects
  • Develop & report KPIs for leadership decision-making
  • Design and implement performance tracking dashboards
  • Lead process improvements and strategic initiatives

Project Portfolio Highlights

Airport Customer Satisfaction

Decoding Nike's Customer Sentiment on Reddit

Examines the surge in U.S. airport complaints since 2020, focusing on Orlando International Airport (MCO), where CLEAR's Expedited Passenger Screening Program has driven dissatisfaction, unlike at airports like Dallas Love Field. Using Department of Transportation data, it proposes to MCO Board Members via PowerPoint that decommissioning CLEAR could cut complaints, boost morale, and raise profits, despite some unfiled complaint data gaps.

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Phishing Link Identifier

Phishing Link Identifier

Machine learning model to detect phishing URLs using XGBoost, achieving high accuracy (99.5%) on a balanced dataset of over 200,000 URLs. This was done by engineering 48 features, such as "suspicious keywords" and the presence of "s" at the end of "http". Python code was written to enable real-world deployment in a browser extension to enhance cybersecurity.

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Decoding Nike's Customer Sentiment on Reddit

Project 3 Screenshot

Exploring customer sentiment about Nike through discussions on Reddit (2022 to 2025). This analysis seeks to understand the root of customer perception issues through Reddit comments using sentiment analysis and topic modeling. Results will help inform Nike's product strategy, branding, and investor relations by identifying key pain points and areas of customer enthusiasm.

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Smart Manufacturing

Smart Manufacturing Project Screenshot

Applied machine learning to minimize unplanned downtime in manufacturing through IoT. By predicting machine failures in advance and identifying root causes, the model enables proactive maintenance, improving productivity and reducing operational costs. The system uses XGBoost for binary and multiclass classification, supported by SHAP for interpretability and SMOTE to address class imbalance.

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SUVs & Pedestrian Deaths

SUV_Capture

Exploring the link between rising U.S. pedestrian deaths and increasing SUV sales. Building on a 2004 study showing SUVs are over twice as deadly to pedestrians as cars, EDA confirms SUV sales strongly correlate with deaths, outpacing cell phone subscriptions in regression models. The study missed deeper analysis of handheld device bans and lacked data on death causes (e.g., distracted driving). Assumptions about passenger car sales were disproven, and data inconsistencies posed challenges.

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Solar Proton Event Prediction

Solar_Capture

Leveraging NiFi, HDFS, Hive, Spark, Spark MLlib, and HBase—to process and predict solar flare magnitudes (Max PFU) using NOAA Space Weather Prediction Center data from 1976 to 2024. Solar flares, massive solar radiation bursts, can disrupt Earth's technology, making accurate, real-time forecasts vital for risk management. The pipeline ingests data via NiFi from a GitHub-hosted CSV, stores it in HDFS, processes it with Hive and PySpark (cleaning, transforming features like latitude), and trains a Decision Tree Regression model in Spark MLlib, achieving an R² of 0.75 and RMSE of 50.23, with latitude as the top predictor. Performance metrics are stored in HBase, demonstrating a scalable framework for real-time solar flare prediction.

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Poverty Prediction

Poverty_Capture

This project supports the United Nations' Sustainable Development Goal #1: the eradication of poverty by 2030. Survey data from Costa Rica, a Gradient Boosting (XGBoost) model was trained to classify households poverty levels. The project aims to assist government programs like *Puente al Desarrollo* in targeting at-risk households more effectively by considering more than just income.

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Shelter Dog Adoptability

Shelter Dog Adoptability Project Screenshot

Analyzing dog adoptability traits, exploring factors influencing adoption speed and reduce shelter euthanizations. Findings show no significant correlation between adoption speed and age (r=0.15) or neutered status (r=-0.19), but a box plot of the top 20 breeds reveals potential patterns—e.g., slower adoptions for Mixed Breeds versus faster for Shih Tzus—offering a starting point for further investigation into breed-specific adoptability factors.

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Netflix Viewership

Netflix_Capture

Analyzing dog adoptability traits, exploring factors influencing adoption speed and reduce shelter euthanizations. Findings show no significant correlation between adoption speed and age (r=0.15) or neutered status (r=-0.19), but a box plot of the top 20 breeds reveals potential patterns—e.g., slower adoptions for Mixed Breeds versus faster for Shih Tzus—offering a starting point for further investigation into breed-specific adoptability factors.

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Loan Applications

Netflix_Capture

Analyizing loan application outcomes with standard classification models. The goal was to determine the best-performing algorithm through model comparison and hyperparameter tuning. The analysis includes K-Nearest Neighbors (KNN), Logistic Regression, and Random Forest classifiers with cross-validation.

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Link to full GitHub Portfolio

Contact Me

ameliacfarrell@gmail.com

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