Resume

Data Scientist · Product Analytics · Statistical Modeling · SQL · Python

Josie Dionne
SF Bay Area, CA · josie@ask-josie.com · (224) 340-8243 · linkedin.com/in/josie-dionne · ask-josie.com
Data Scientist | Product Analytics | Statistical Modeling | SQL | Python
Summary

Data scientist and analytics leader with 10+ years of experience translating complex business questions into data-driven insights that improve products, operations, and strategic decision-making. Experienced building scalable SQL data models, statistical analyses, predictive analytics, KPI frameworks, and executive dashboards that influence multi-million-dollar business decisions. Strong background partnering with product, engineering, finance, and operations teams to define metrics, uncover customer behavior, improve forecasting, and deliver actionable recommendations through data.

Technical Skills
Languages: SQL · Python
Libraries: Pandas · NumPy · scikit-learn
Visualizations: Tableau · Excel
Analytics: Statistical Analysis · Predictive Analytics · Forecasting · Data Modeling · Feature Engineering · Customer Behavior Analysis · KPI Development
Experience
Manager, Research & Analytics | Applied Data Science March 2024 – Present
Urban Economics · Remote
  • Lead a team of four analysts delivering 40 quarterly market intelligence reports across 12 metropolitan markets supporting institutional clients managing over $2B in real estate assets.
  • Partner with engineering and business stakeholders to define analytics requirements, prioritize reporting capabilities, and improve automated market intelligence products.
  • Design reporting workflows that reduced appraisal revision cycles by 25% while improving data quality and operational efficiency.
  • Analyze large-scale market datasets to identify trends, quantify risk, and deliver recommendations supporting institutional investment decisions.
  • Perform statistical analyses, feature engineering, market segmentation, and model validation to improve valuation accuracy across large real estate datasets.
  • Translate complex analytical findings into clear recommendations supporting investment strategy and executive decision-making.
Manager, Business Intelligence November 2021 – February 2024
Compass Group USA (E15 Group) · Chicago, IL · Promoted from Senior Analyst, Business Strategy
  • Designed scalable SQL data models reducing approximately 6 billion source records to 14 million analytics-ready records, powering six enterprise dashboards while maintaining consistent business logic across a 2,000+ location organization.
  • Architected SQL and Python data pipelines integrating financial, operational, labor, and purchasing data to support enterprise reporting and analytics.
  • Embedded row-level security within enterprise reporting solutions, enabling scalable, role-based access without duplicating business logic.
  • Developed executive dashboards, KPI frameworks, and automated reporting that improved financial visibility and operational decision-making across regional leadership.
  • Analyzed customer purchasing behavior, pricing trends, category performance, and product mix to identify revenue opportunities and improve commercial strategy.
  • Led and mentored four analysts while managing a twenty-person internship program and delivering cross-functional analytics initiatives.
Regional Director of Operations January 2016 – October 2021
Quest Events · Events & Hospitality · Chicago, IL
  • Developed a data-driven pricing model using multi-year customer demand and purchasing behavior, contributing to 15.4% annual revenue growth while maintaining 0% customer churn.
  • Applied operational analytics to reduce overtime costs by 18% through workforce optimization and process improvements.
  • Directed operations across more than ten locations and over one hundred employees using performance metrics, forecasting, and operational reporting to improve business outcomes.
  • Designed analytics-informed onboarding and training programs that reduced onboarding time by 30%.
Selected Data Science Projects
Predictive Modeling for Congestive Heart Failure Classification — University of Delaware
  • Built and evaluated multiple supervised machine learning models to predict congestive heart failure using a clinical dataset of 5,888 patient records.
  • Prepared and analyzed clinical data in Python using Pandas and NumPy before training machine learning models in scikit-learn.
  • Compared Decision Tree, Random Forest, Logistic Regression, Naïve Bayes, KNN, SVM, and Deep Neural Network models using accuracy, precision, recall, and F1 score.
  • Achieved 95.3% accuracy with the best-performing model while evaluating tradeoffs between interpretability and predictive performance.
Functional Annotation of Pseudomonas Phage PRR1 gp1
  • Applied bioinformatics and statistical analysis to characterize an unannotated bacteriophage protein.
  • Integrated sequence analysis, structural prediction, and literature review to identify likely biological function.
  • Produced reproducible computational analyses supporting functional annotation.
Education
M.S., Bioinformatics Data Science — University of Delaware In Progress
Relevant Coursework: Statistical Learning · Machine Learning · Predictive Modeling · Data Mining · Bioinformatics
B.S., Biology — Northeastern Illinois University · 3.7 GPA 2016