KshafayetCV

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% Header with name and contact information
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% Name at top of CV
\cvname{Shafayet Khan Shafee}

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% \contactbar{Email}{Website}{GitHub}
%            {LinkedIn}{Google Scholar URL}
%            {Phone}{Location}
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\contactbar{sshafee@isrt.ac.bd}{kshafayet.netlify.app}
           {shafayetShafee}{shafayetshafee}
           {https://scholar.google.com/citations?user=GSgDEx0AAAAJ&hl}
           {+880-1791-051104}
           {Dhaka, Bangladesh}


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% Short Intro
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\vspace{10pt}

Statistician and data scientist applying rigorous statistical methods to public
health research and industry problems, with a commitment to reproducible research
and open-source software development.

\vspace{-2pt}

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% Research Interests
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\section*{Research Interests}

Causal Inference, Medical Statistics, Epidemiology, Health Data Science,
Causal Mediation Analysis, Survival Analysis, Hierarchical Modeling, Bayesian Inference,
Statistical Machine Learning.


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% Education
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\section*{Education}

\edublock[
  \item \textbf{Thesis:} Developed point and interval estimators extending the
  median odds ratio framework for quantifying between-cluster variation in three-level
  hierarchical binary data, evaluated their statistical performance through extensive
  Monte Carlo simulation studies, and implemented the methodology in an open-source
  R package for post-estimation inference.
  \item \textbf{Selected Coursework:} Causal Inference; Multilevel Modeling;
  Bayesian Inference; Spatial Statistics; Statistical Machine Learning.
]
{M.Sc. in Applied Statistics}
[GPA 3.97 / 4.00]
{Institute of Statistical Research \& Training}
[University of Dhaka]
{Dhaka, Bangladesh}
{2022--2023}

\vspace{8pt}

\edublock[
  \item \textbf{Project:} Examined the association between caregivers' stimulation
        activities and socio-emotional development in under-five children using
        nationally representative MICS 2019 Bangladesh data and survey-weighted
        logistic regression analysis.
  \item \textbf{Selected Coursework:} Statistical Inference; Sampling Methods;
        Multivariate Statistics; Generalized Linear Models; Analysis of Time Series;
        Design \& Analysis of Experiments; Epidemiology; Survival Analysis; Econometrics;
        Actuarial Statistics; Industrial Statistics \& Operations Research.
]
{B.Sc. in Applied Statistics}
[CGPA 3.96 / 4.00]
{Institute of Statistical Research \& Training}
[University of Dhaka]
{Dhaka, Bangladesh}
{2018--2022}


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% Experience
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\section*{Professional Experience}

\cvblock[
  \item Applied Bayesian experimental design (Bayes Factor Design Analysis)
        to estimate sample sizes for controlled product experiments,
        incorporating prior information and evidence-based stopping criteria.
  \item Designed and analyzed randomized controlled experiments, including
        randomization checks through covariate balance assessment and Sample
        Ratio Mismatch (SRM) testing, to evaluate business initiatives.
  \item Estimated causal effects of a fintech product intervention on merchant
        transaction behavior using propensity score matching and
        Difference-in-Differences.
  \item Applied Hierarchical Bayesian modeling to evaluate adoption across
        cohorts in a controlled experiment, accounting for small and unequal
        group sizes and quantifying uncertainty through posterior distributions.
  \item Applied causal ML methods, namely Double ML and generalized random
        forests, to estimate heterogeneous treatment effects across user
        subgroups, supporting targeted intervention design.
  \item Co-developed a repayment risk scoring framework using Random Survival
        Forests to model time-to-repayment distributions, drawing on
        survival analysis methods for a credit risk application.
  \item Built scalable analytical pipelines using dbt and BigQuery, supporting
        reproducible statistical workflows and standardized automated analytical
        reporting.
]{Data Scientist}{Dhaka, Bangladesh}{Pathao Limited}{Jul 2023--Present}


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% Publications
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\section*{Publications}

% Define note explaining publication symbols for author annotations
\defbibnote{symbols}{$^\dagger$ Corresponding author \enspace $^*$ Equal contribution}

\nocite{*}
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% Honors & Awards
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\section*{Awards \& Achievements}

\begin{itemize}
  \awarditem{Dean's Award}
            {Faculty of Science, University of Dhaka}
            {2025}

  \awarditem{Conference Award for Scientists}
            [for the abstract ``Interval Estimation of the Median Odds
            Ratio for Measuring Contextual Effects in Multilevel Data Using a
            Binary Logistic Model'']
            {45th Annual Conference of the International Society for Clinical
            Biostatistics (ISCB45), Thessaloniki, Greece}
            {2024}

  % \awarditem{Datathon Finalist}
  %           [Ranked among the top 10 teams out of 300; developed an algorithm for
  %           predicting mobile data packages purchase behavior]
  %           {Robi Axiata Limited, Dhaka, Bangladesh.}
  %           {2024}

  \awarditem{National Science and Technology (NST) Fellowship}
            [for M.Sc.\ thesis research on multilevel modeling]
            {Ministry of Science and Technology (MoST), Government of Bangladesh}
            {2023}
\end{itemize}


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% Talks & Presentations
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\section*{Talks \& Presentations}

\begin{itemize}
  \cvitem{``On the Estimation of the Median Odds Ratio for Measuring Contextual
          Effects in Multilevel Binary Data from Complex Survey Designs''}
         {International Conference on Applied Statistics and Data Science
          (ICASDS 2025), University of Dhaka, Bangladesh}
         {2025}
\end{itemize}


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% Open Source Contributions
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\section*{Open Source Contributions}

\subsection*{R Packages}

\begin{itemize}[itemsep=3pt, parsep=0pt, topsep=0pt, leftmargin=*]

  \item \href{https://shafayetshafee.github.io/MOR/}{\texttt{MOR}} --- Post-estimation
  functions to compute the Median Odds Ratio and corresponding confidence interval
  from fitted multilevel binary logistic regression models.

  \item \href{https://shafayetshafee.github.io/skmisc/}{\texttt{skmisc}} --- Miscellaneous
  utility R functions.

\end{itemize}

\subsection*{Python Packages}

\begin{itemize}[itemsep=3pt, parsep=0pt, topsep=0pt, leftmargin=*]

  \item \href{https://skmiscpy.readthedocs.io/en/latest/}{\texttt{skmiscpy}} ---
   Reusable functions for causal inference diagnostics, including mirror histograms,
   Love plots, and standardized mean difference (SMD) computation for inverse
   probability weighting (IPW).

  \item \href{https://github.com/shafayetShafee/kedrogen}{\texttt{kedrogen}} ---
  CLI tool for scaffolding reproducible data science projects from cookiecutter templates.

\end{itemize}


\subsection*{Miscellaneous}

\begin{itemize}[itemsep=3pt, parsep=0pt, topsep=0pt, leftmargin=*]

  \item \href{https://github.com/shafayetShafee/randomizer}{\texttt{randomizer}} ---
        Dockerized Shiny application for random allocation of experimental units
        into treatment groups with specified proportions, featuring SRM testing
        and covariate balance diagnostics via Love plots.

  \item Developed multiple \href{https://quarto.org/}{Quarto} extensions to
        enhance scientific documents and presentations, including tools for code
        highlighting (\href{https://github.com/shafayetShafee/line-highlight}{\texttt{line-highlight}}),
        interactive SQL demonstrations (\href{https://github.com/shafayetShafee/interactive-sql}{\texttt{interactive-sql}}),
        embedding downloadable resources (\href{https://github.com/shafayetShafee/downloadthis}{\texttt{downloadthis}}),
        and presentation styling (\href{https://github.com/shafayetShafee/reveal-header}{\texttt{reveal-header}}),
        available via the \href{https://quarto.org/docs/extensions/}{Quarto Extension Registry}.

  \item \href{https://github.com/shafayetShafee/python-uv-gcloud}{\texttt{python-uv-gcloud}} ---
        Minimal Docker base image with Python, \href{https://github.com/astral-sh/uv}{\texttt{uv}}, and
        Google Cloud SDK for CI/CD pipelines and cloud workflows.

  \item \href{https://github.com/shafayetShafee/uvshot}{\texttt{uvshot}} ---
        Bash scripts for reproducible and isolated Python environment setup
        using \texttt{uv}.

\end{itemize}


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% Technical Skills
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\section*{Technical Skills}

\begin{itemize}[itemsep=3pt, parsep=0pt, topsep=0pt, leftmargin=*]

  \item \textbf{Programming:} R, Python, SQL, Lua, Octave, Julia.

  \item \textbf{Statistical Software:} Stata, SAS, Minitab, SPSS.

  \item \textbf{Machine Learning:} Ensemble Models, Causal ML (DML, GRF, BCF),
        Model Calibration, Conformal Prediction.

  \item \textbf{Data \& MLOps:} Shiny, BigQuery, Google Data Studio, GCP,
        MixPanel, dbt, Kedro, MLflow, Docker, Bash.

  \item \textbf{Others:} \LaTeX{}, Quarto, Rmarkdown, Git, GitHub Actions, GitLab CI.

\end{itemize}


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