Krystian Korzec

Full-Stack Data Scientist | Causal Inference & Bayesian Modeling

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Professional Summary

Quantitative Full-Stack Data Scientist with over 8 years of experience bridging rigorous statistical methodology with production-grade software engineering. Specialized in Causal Inference, Bayesian Statistics, and mathematical optimization. Proven track record of developing end-to-end analytical solutions—from estimating heterogeneous treatment effects (CATE) to architecting containerized, production-ready web applications (PyShiny) and scalable data pipelines. Adept at managing the full lifecycle of data products using a modern cloud and database stack.

Key Competencies & Tech Stack

  • Causal Inference & Experimentation: Heterogeneous Treatment Effect (CATE) estimation, Double Machine Learning, A/B Testing, Synthetic Controls, Geo-experimentation, Uplift Modeling.
  • Statistical & Optimization Methods: Hierarchical Bayesian Modeling, MCMC simulation, Mixed-Integer Linear Programming, Stochastic Optimization (SAA).
  • Programming & Libraries: Python (Pandas, Polars, Scikit-learn, PyMC, Pyomo, PyShiny), R, Advanced SQL, PySpark.
  • Cloud, Infrastructure & Databases: GCP (BigQuery), AWS, Snowflake, MongoDB, Redis, Docker, CI/CD, Git, Airflow.

Employment History

Data Scientist | Allegro

2024 Jul – Present | Warsaw, Poland

  • Formulated and deployed hierarchical Bayesian predictive models and Sample Average Approximation workflows (Project Charon) for real-time contact allocation under operational uncertainty.
  • Co-architected the methodology for geographic experimentation frameworks (geo-testing/synthetic controls) to robustly measure the causal impact of pricing interventions.
  • Developed causal pricing models to estimate price elasticity (CATE) and optimize product selections, moving beyond pure prediction to direct counterfactual policy optimization.
  • Tech Stack: Python, PyMC, Pyomo, GCP, BigQuery, GitHub, Airflow.

Contract ML Engineer (Part-Time / B2B) | Roche (via Seargin)

2024 Aug – 2026 Apr | Remote

  • Designed and deployed a full-stack, AI-driven platform (PyShiny web app) to automate the generation of complex non-clinical statistical reports, significantly reducing manual research overhead.
  • Architected the application infrastructure using containerized deployments (Docker) integrated with robust CI/CD pipelines.
  • Implemented state management using Redis and established comprehensive testing to ensure the high performance and reliability of the analytical tools.
  • Tech Stack: Python, R, PyShiny, Docker, GitLab, Redis, Gemini API.

Data Scientist | Allianz

2023 Jul – 2024 Jul | Warsaw, Poland

  • Developed and maintained data-intensive statistical applications and analytical pipelines using Python (PySpark, Pandas).
  • Utilized Amazon EMR Studio to perform large-scale data processing tasks for actuarial and analytical workflows.
  • Tech Stack: Python, AWS, GitHub, SQL.

Data Science Analyst | Accenture

2022 Nov – 2023 Jul | Warsaw, Poland

  • Delivered analytical solutions solving complex allocation problems using Integer Programming (Pyomo) and Supply Chain Optimization techniques.
  • Spearheaded the refactoring of legacy statistical R code into scalable PySpark/R solutions in Azure Databricks, drastically reducing execution time and enforcing engineering rigor.
  • Tech Stack: Python, GCP, Databricks, GitHub, PySpark.

Data Scientist | Nowa Era

2021 Jan – 2022 Sep | Warsaw, Poland

  • Designed and deployed causal uplift models (Heterogeneous Treatment Effects) for targeted retention campaigns, successfully isolating the true incremental impact of marketing interventions from organic behavior.
  • Built predictive models for early detection of at-risk users, transitioning the analytical focus towards proactive intervention strategies.
  • Architected analytical infrastructure and end-to-end data pipelines leveraging AWS and Snowflake.
  • Tech Stack: Python, R, Snowflake, GitHub, Docker, AWS.

Data Scientist | PKO BP

2018 Jul – 2021 Jan | Warsaw, Poland (Promoted to Data Scientist following a 6-month Data Science Internship)

  • Utilized spatial analytics and statistical segmentation to build Automated Valuation Models (AVM) for real estate pricing prediction.
  • Engineered automated monitoring pipelines to track model stability and detect geographic feature drift.
  • Tech Stack: Python, Hadoop, SQL.

Education

Warsaw School of Economics (SGH)

Master’s Degree in Quantitative Methods in Economics and Information Systems (MIESI) - Master Thesis: Spatio-Temporal Bayesian Model of PM2.5 and PM10 Air Pollution

University of Warsaw (MIMUW)

Completed 2 Years of B.Sc. Coursework in Pure Mathematics - Built a rigorous theoretical foundation in probability theory, real analysis, and linear algebra supporting advanced statistical modeling.