π Data Science Graduate Student @ Duke | π€ AI Explainability & Causal ML | π Data-Driven Decision Making
I am a data scientist with expertise in machine learning, causal inference, NLP, and statistical experimentation. My work bridges the gap between AI interpretability, data-driven business decisions, and real-world applications.
π Passionate about explainable AI, ethical ML, and disinformation research
π Strong background in data analytics, experimentation, and machine learning
π΅ Enthusiast in network analysis and data visualization
πΉ Duke University (Aug 2023 β May 2025)
M.S. in Interdisciplinary Data Science
- Coursework: Machine Learning, Deep Learning, NLP, Causal Inference, Bayesian Statistics, Data Engineering
πΉ Higher School of Economics, Moscow
B.S. in Political Science (Quantitative Methods major, Applied Statistics minor)
- Coursework: Data Analysis, Machine Learning, Econometrics, Advanced Statistical Modeling
- Recipient of Federal full-tuition scholarship
- Nominee for Presidential Grant in Science
πΉ Web Data Scientist @ ROI Revolution
- Developed machine learning models (Gradient Boosting, SHAP) to optimize eCommerce conversion rates
- Designed A/B testing frameworks (sequential, Bayesian) and analyzed 100+ experiments
- Built data pipelines using Google Analytics API & BigQuery for enhanced business intelligence
πΉ Middle Data Analyst @ Tinkoff Bank
- Led the development of BI dashboards for executive decision-making
- Conducted statistical experimentation to reduce client loss, saving ~$700K/month
- Built a real-time payment prediction ML model for Fintech workflows
π» Languages: Python, SQL, Rust, VBA, R
π Data Science & ML: Scikit-Learn, TensorFlow, PyTorch, NLP, Bayesian Statistics
π Visualization & Analytics: Plotly, Dash, Tableau, Google Data Studio
β‘ Experimentation: A/B Testing (Bayesian, Frequentist, Sequential), UX Testing
π§ anastasiia.saenkoduke@gmail.com
π GitHub | LinkedIn | Portfolio