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  • Columbus, OH

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DivyaSharma0795/README.md

Divya Sharma - Data Scientist | Marketing Analytics & Experimentation

MS in Data Science (Duke University) · 7+ years across Citizens Bank, Amazon, and Mu Sigma

I specialize in marketing measurement and experimentation - using A/B testing, incrementality, causal inference, and statistical modeling to understand what actually drives customer response and inform targeting and budget decisions.

Core Focus

  • Experimentation - A/B testing, factorial designs, incrementality measurement, power analysis & test feasibility
  • Causal Inference - lift estimation, difference-in-differences, treatment effects
  • Marketing Analytics - campaign measurement, response curves, offer optimisation
  • Statistical Modelling - logistic regression, segmentation, distribution analysis

Portfolio

Project Methods Domain
geo-measurement Generative Engine Optimization · Wilcoxon Test Marketing Optimization
streaming-causal-impact Causal Inference · Diff-in-diff Campaign Analytics
streaming-campaign-measurement Logistic Regression · Contrast Testing · Kruskal-Wallis Campaign Measurement
ab-test-feasibility MDD · Power Analysis · Sample Size Planning · Multiple-Comparison Correction Experiment Design
Explainable AI Portfolio SHAP · PDP · Saliency Maps · Mechanistic Interpretability Model Explainability
Apple Vision Pro Sentiment Analysis DistilBERT · RoBERTa · VADER · TextBlob NLP
(more coming)

Background

Decision Scientist at Mu Sigma → Business Analyst at Amazon India → MS Data Science at Duke → Data Scientist at Citizens Bank

I started my career in decision science and business analytics before moving into data science, giving me a strong focus on translating statistical analysis into business decisions.

Full Skillset

Statistics & Experimentation A/B Testing · Experimental Design · Power Analysis · Hypothesis Testing · Incrementality · Causal Inference · Difference-in-Differences · Regression · Mixed Effects Models

Marketing Analytics Campaign Measurement · Response Modeling · Customer Targeting · Marketing Mix Modeling · Budget Optimization

Tools & Platforms Python · SQL · R · pandas · statsmodels · scikit-learn · scipy · Git · Excel · Tableau

Machine Learning & AI Propensity Modeling · Classification · SHAP · PyTorch · TensorFlow · HuggingFace · NLP · Explainable AI · Sentiment Analysis · Deep Learning

Domain Experience Marketing Analytics · Financial Services · E-commerce · Retail · Insurance · Asset Management

Industry Experience

  • Citizens Bank - Campaign measurement, A/B testing, response curves, incrementality analysis for banking products
  • Amazon India - Retail loss prevention analytics, financial reporting, Fresh grocery expansion
  • Mu Sigma - Decision science across insurance, asset management, retail, and CPG clients

LinkedIn

📧 divyasharma0795@gmail.com

Pinned Loading

  1. ab-test-feasibility ab-test-feasibility Public

    Check whether your A/B test can detect a real difference before you run it. MDD & sample-size calculator with Streamlit, web, and Excel versions.

    HTML

  2. geo-measurement geo-measurement Public

    Measuring whether Generative Engine Optimization (GEO) increases AI citation likelihood: a paired LLM-as-judge experiment in Python.

    Python 1

  3. streaming-causal-impact streaming-causal-impact Public

    Causal impact of a competitor launch on streaming churn: Difference-in-Differences with a justified control group and pre-trend checks.

    Jupyter Notebook 1

  4. AppleVisionPro_Dataset AppleVisionPro_Dataset Public

    Sentiment analysis of Apple Vision Pro tweets using rule-based and transformer-based models to compare speed, accuracy, and contextual understanding

    Jupyter Notebook 3

  5. Explainable_AI Explainable_AI Public

    A curated portfolio of Explainable AI (XAI) projects exploring model interpretability across classical machine learning, deep learning, and large language models.