Jessica Spencer

About Me

As a seasoned Data Scientist, I harness the power of data to address critical social challenges. My expertise spans statistical analysis, machine learning, data visualization, and software development, with proficiency in Python, R, and SQL. I specialize in transforming complex data into clear, actionable insights that drive strategic decision-making and meaningful change.

My academic background, with a Master’s in Applied Statistics and a dual Bachelor’s in American Studies and Computer Science, provides me with a multifaceted understanding of how data intersects with social issues. This expertise is complemented by hands-on experience in developing solutions that enhance data integrity, project efficiency, and organizational effectiveness.

Certified as both a Rape Crisis and Domestic Violence Counselor, I am deeply committed to using data science to promote equity and justice. I seek opportunities to collaborate with organizations and professionals who are passionate about leveraging technology to improve lives and strengthen communities.

If you are interested in making data work for social good, I invite you to connect with me on LinkedIn and explore how we can make a positive impact together.

Skills

Projects

Gavi Vaccination Impacts: A Propensity Score Study

R Causal Inference Propensity Score Matching

Causal analysis of the Global Alliance for Vaccines and Immunisation using a doubly-robust propensity score design. Analyzed the impact on mortality rates in countries with less than $1000 gross net income.

Volunteering and Pet Ownership?

Python Data Cleaning Missingness Analysis Data Visualization Logistic Regression Prediction

I analyzed a dataset from Edmonton on demographics, pet ownership, and volunteering. While higher education had a small positive link to volunteering, pet ownership did not relate to volunteering, and the model had limited explanatory power.

Increasing Contraceptive Choice: Exploring Effective Intervensions in Indonesia

R Impact Analysis Multiple Logistic Regression Prediction

Contraceptives are a transformative tool for health and life opportunities for those capable of pregnancy. In this analysis, I investigate interventions to increase contraceptive use, and find that education increases contraceptive use, specifically long term, across ages.

Time Series Causal Analysis

Python Time Series Analysis Granger Causality

Conducted Granger causality analysis on the relationship between residential construction costs and number of households in the US over time, revealing a strong correlation between the two factors.

Unicorn Companies: SQL Analysis

SQL Data Analysis

In this project, I produced PostgreSQL to understand which industries are producing the highest valuations and the rate at which new high-value companies are emerging.

Surveying Mindfulness Techniques in NYC Education

Survey Design Survey Administration Research Literature Review Qualitative Analysis

As part of my master's degree, I designed a survey to study the effects of mindfulness intervention programs on students in New York City schools across different racial, gender, and socio-economic backgrounds.

Relating Physical Activity to Problematic Internet Use

Multi Class Classification Time Series Analysis NVIDIA cuDF GPU DataFrame

In this Kaggle competition hosted by the Child Mind Institute, the goal is to develop a predictive model that analyzes children's physical activity and fitness data to identify early signs of problematic internet use. In my first submission, I improved the data load step by making it modular.

Education