बहुत बढ़िया सीवी स्टाइल

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\begin{center}
{\fontsize{30}{34}\selectfont\bfseries\color{accent} Marta Kowalski}\\[3pt]
{\Large Data Scientist}\\[6pt]
{\small [email protected] \quad Krakow, Poland \quad linkedin.com/in/mkowalski \quad github.com/mkowalski}
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\sect{Experience}
\role{Senior Data Scientist}{Baltic Analytics}{2021 -- present}
\begin{itemize}
  \item Built the demand forecasting model that cut warehouse overstock by 18 percent across 40 stores.
  \item Lead a team of four and set the review standards for all production machine learning code.
\end{itemize}

\role{Data Scientist}{Vistula Retail Group}{2018 -- 2021}
\begin{itemize}
  \item Shipped a churn prediction pipeline serving 2 million customer scores weekly.
  \item Automated the monthly reporting suite, saving the analytics team two days per cycle.
\end{itemize}

\sect{Education}
\role{M.Sc. Statistics}{Jagiellonian University}{2016 -- 2018}
\role{B.Sc. Mathematics}{AGH University of Krakow}{2012 -- 2016}

\sect{Skills}
\textbf{Languages:} Python, R, SQL, Scala\\[2pt]
\textbf{Tools:} scikit-learn, PyTorch, Airflow, Spark, dbt\\[2pt]
\textbf{Focus areas:} forecasting, causal inference, experimentation

\sect{Publications}
\begin{itemize}
  \item Kowalski, M. et al. Robust demand forecasting under promotional shocks. Journal of Retail Analytics, 2023.
\end{itemize}

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