I’m a Causal Marketing Measurement Scientist specializing in Bayesian Marketing Mix Modeling (PyMC), geo-based incrementality testing, and budget optimization.

I build and operationalize scalable MMM frameworks that translate complex posterior outputs into real-world media investment decisions. My work focuses on experiment-anchored modeling, hierarchical geo-level MMMs, and turning decision science into repeatable systems.

• Bayesian hierarchical MMM in PyMC
• Geo holdout experiment design
• Incrementality calibration of MMM
• Budget optimization under uncertainty
• Scalable modeling workflows and automation

Causal & Econometric Modeling

• Bayesian MMM (PyMC)
• Hierarchical modeling
• Bayesian Structural Time Series (CausalImpact)
• Incrementality testing & geo experiments
• Budget optimization & scenario modeling

Programming

• Python (PyMC, ArviZ, Pandas, NumPy)
• SQL

Cloud & Data

• Google BigQuery
• AWS
• Databricks
• MLFlow

Visualization & Communication

• Matplotlib
• Seaborn
• Plotly
• Tableau

Research, Communication, Accountability, Initiative, Collaboration, Critical Thinking, Passion, Presentation, Project Delivery, Idea Generation

• My main portfolio website that contains the following data science projects

• Analyzed the impact of a "Delivery Club" special on sales for a grocery retailer using a Python causal impact library, revealing a 41.1% uplift in sales.

• Developed a predictive model for Parkinson's severity using boosted tree models with feature engineering, resulting in significant improvement in F1 score and recall.

• Analyzed customer buying patterns to uncover product relationships in alcohol retail, revealing insights about customer preferences.

• Predicted customer behavior using historical music sales data, achieving high accuracy through feature space compression and Random Forest.

• Segmented customers for a grocery chain to provide marketing insights based on dietary preferences.

• Created a dashboard for targeted marketing campaigns based on bank customer demographics.

• Boosted tree models for predicting the maximum severity of Parkinsons for clinical patients based on their protein and peptide mass spectrometry quantities.
• Explainable results using Feature Importance for each patient and a description of the top proteins.

• Uses an XGBoost Classifier to predict how likely an employee will leave based on the HR data.
• Provides SHAP values to explain the probability score assigned to the employee.

• Used DenseNet-201 pre-trained neural network and fine-tuned one hidden layer with 4096 nodes and 30% dropout for regularization.
• Solved imbalanced target distribution using class weights to the parameters for the neural network.
• Applied hyperparameter tuning of # of hidden layers, nodes, learning rate, batch size, learning rate decay, and momentum to find optimal values.
• Final Test Statistics:
  • AUC: 0.990
  • F1: 0.972
  • Recall: 0.971
  • Precision: 0.973