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bengtsoderlund/README.md

Bengt Söderlund

I'm an Assistant Professor of Economics transitioning into a data science career. My background is in international trade and applied econometrics, with extensive experience in empirical modeling, causal inference, and working with large-scale structured datasets.

I’ve recently built several Python, SQL, and R projects that apply machine learning, causal inference, and financial data analysis to real-world problems. You’ll find a selection of these projects below:

  • Financial data pipeline and KPI analysis using API and SQL
  • Predictive modeling of late shipments using Python and machine learning
  • Causal impact evaluation of the EU–Ukraine FTA using a dynamic gravity model in R

Technical Skills

Languages & Tools: Python, SQL, R, Git, Jupyter, Stata
Libraries: pandas, scikit-learn, NumPy, matplotlib, Seaborn, requests, sqlite3
Core Areas: Machine Learning, Causal Inference, Data Wrangling, Big Data Analysis, Econometric Modeling

More About Me

I'm currently based in Sweden and will be relocating to the U.S. in 2026. I will have full work authorization through a spousal green card and won’t require employer sponsorship.

For academic publications and teaching, visit my current research website. I plan to add data science content there in the future.

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  1. bengtsoderlund bengtsoderlund Public

    GitHub profile README highlighting my background in applied economics, data science transition, and key technical skills.

    1

  2. EU-Ukraine-Trade-Analysis EU-Ukraine-Trade-Analysis Public

    Report for the National Board of Trade Sweden evaluating the impact of the EU-Ukraine free trade agreement on trade flows using a gravity model.

    R 1

  3. late-shipment-prediction-ml late-shipment-prediction-ml Public

    Develops two machine learning classifiers using Python to help a global sports and outdoor equipment retailer identify high-risk shipments before delays occur.

    Jupyter Notebook 1

  4. sql-healthcare-financials sql-healthcare-financials Public

    Analyze U.S. healthcare companies using Python, SQL, and API-based financial data; includes database setup, KPIs, and visualizations.

    Jupyter Notebook 1