About Me

I am a data scientist with a passion for transforming complex datasets into actionable insights. Having recently graduated from Carleton College with a Bachelor's Degree in Statistics, I have hands-on experience applying machine learning, data visualization, and statistical methods across diverse industries. During my internships with NASA Goddard Space Flight Center and SK Huffer & Associates, I tackled challenges ranging from soil moisture imputation using supervised machine learning to eDiscovery data analysis for litigation support. My academic and professional journey reflects a commitment to combining technical skills in Python, R, and SQL with innovative problem-solving to drive impactful solutions. Beyond academics, I bring leadership as a former team captain of Carleton's football team and a former member of the Carleton Leadership Council, balancing technical expertise with teamwork and collaboration. Whether developing interactive applications or optimizing workflows, I aim to bridge data and decision-making to create meaningful change.

  • Data Science
    Machine Learning, Time Series Analysis, Regression Analysis
  • Data Storytelling
    Data Wrangling, Data Visualization
  • Programming
    R, Python, SQL, Jupyter Notebooks, GitHub
  • June 2024 - August 2024
    Data Science Intern at NASA Goddard Space Flight Center
  • April 2024 - June 2024
    eDiscovery/Machine Learning Intern at SK Huffer & Associates
  • June 2022 - August 2022
    Data Analyst Intern at MIAC Analytics
  • June 2021 - August 2021
    Data Analyst Intern at A4 Training
  • 2020 - 2024
    Bachelor's in Statistics from Carleton College

My Services

Data Analysis

Performing predictive modeling, supervised machine learning, EDA, and time series analysis

Data Processing

Efficiently collecting data through web-scraping and research. Performing data imputation, transformation, and cleaning.

Data Storytelling

Creating compelling visuals and dashboards to communicate insights effectively to diverse audiences.

My Work

Heart Disease Prediction App

Interactive R Shiny application to predict cardiovascular disease based on user-input parameters

Gridiron Fortunes: Analyzing the NFL Prospects of College Quarterbacks

Exploring the potential to predict NFL quarterback success through an analysis of collegiate performance statistics

In situ Soil Moisture Observation Network Optimization

Using SMAP soil moisture to optimize the design of the UMRB in situ station network

Contact Me

chriselliott7152@gmail.com

443-359-0553

Download Resume