Skip to content
View pawel-zajac-dev's full-sized avatar
🌎
Working from home
🌎
Working from home
39 followers · 13 following

Block or report pawel-zajac-dev

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Content in all repositories owned by your account will be closed.
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
pawel-zajac-dev/README.md
GitHub Loop

Hi, I'm Paweł

Machine Learning Engineer & Data Scientist

Focusing on interpretable, probabilistic, and statistically sound modeling.

LinkedIn Email

About Me

I combine machine learning, Bayesian inference, and stochastic modeling to analyze complex, noisy datasets and simulate dynamic systems.

  • Current Role: Pay Equity Specialist / Data Analyst at Mercer (statistical modeling in workforce & compensation analytics).
  • Core Philosophy: Designing models that explain the mechanisms behind data, not just black-box predictions.
  • Research & Practice: Extracting actionable signal from uncertainty and non-stationary processes.

Tech Stack & Tooling

Tech Stack

Areas of Focus

Specializations

  • Probabilistic & Bayesian ML: MCMC, MAP, Variational Inference, Bayesian Regression, Uncertainty Quantification

  • Reinforcement Learning: Bandits (UCB, Thompson Sampling), Exploration–Exploitation frameworks

  • Time Series & State-Space: ARIMA/VAR, GARCH, Hidden Markov Models, Kalman filtering

  • Stochastic Processes: DTMC/CTMC, Wiener processes, Gaussian processes

  • Inference & Experiments: A/B testing frameworks, Frequentist vs. Bayesian experimental design

  • ML Engineering: Reproducible pipelines, Bayesian optimization, model evaluation

Applied Interests

  • Recommender Systems: Latent factor models, collaborative & content-based filtering

  • Applied NLP: Semantic embeddings, LSI, representation learning

  • Representation & Dynamics: Dimensionality reduction, feature decomposition

  • Decision Modeling: Simulation of market & human capital dynamics under uncertainty

Pinned Loading

  1. Machine-Learning-Deep-Learning Machine-Learning-Deep-Learning Public

    This repository contains implementations of core machine learning algorithms written from scratch, following their mathematical formulations rather than relying on high-level libraries like scikit-…

    Jupyter Notebook 7

  2. Bayesian-Probability-Statistics-Machine-Learning Bayesian-Probability-Statistics-Machine-Learning Public

    This project explores data analysis, blending core Probability Theory and Descriptive Statistics with Statistical Inference and Bayesian Machine Learning (Regression/Classification). It concludes w…

    Jupyter Notebook 4

  3. Time-Series-Models Time-Series-Models Public

    Time series analysis and forecasting with statistical models, exponential smoothing, and curve-fitting techniques.

    Jupyter Notebook 3

  4. Stochastic-Processes Stochastic-Processes Public

    Markov chains, DTMCs, HMMs, Gaussian HMMs, higher-order HMMs, Poisson & Wiener processes, MDPs, etc.

    Jupyter Notebook 3

  5. Reinforcement-Machine-Learning Reinforcement-Machine-Learning Public

    Markov Decision Processes, Q-learning, Policy Gradients, Actor-Critic, Exploration–Exploitation

    Jupyter Notebook 3

  6. NLP-Recommender-Systems NLP-Recommender-Systems Public

    Jupyter Notebook 3