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Hello, I'm Maryam Bayat Makou, a Data Scientist based in Geneva, Switzerland. I bring hands-on experience in machine learning, large-scale data analysis, and explainable AI to turn complex data into actionable insights. I enjoy collaborating with cross-functional teams to build interpretable models and communicate results to both technical and non-technical stakeholders, supporting operational and strategic decision-making in fast-paced environments.…Hello, I'm Maryam Bayat Makou, a Data Scientist based in Geneva, Switzerland. I bring hands-on experience in machine learning, large-scale data analysis, and explainable AI to turn complex data into actionable insights. I enjoy collaborating with cross-functional teams to build interpretable models and communicate results to both technical and non-technical stakeholders, supporting operational and strategic decision-making in fast-paced environments.

Maryam Bayat Makou

Data Scientist, AI Engineer, Data Analyst, +2

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Hello, I'm Maryam Bayat Makou, a Data Scientist based in Geneva, Switzerland. I bring hands-on experience in machine learning, large-scale data analysis, and explainable AI to turn complex data into actionable insights. I enjoy collaborating with cross-functional teams to build interpretable models and communicate results to both technical and non-technical stakeholders, supporting operational and strategic decision-making in fast-paced environments.…Hello, I'm Maryam Bayat Makou, a Data Scientist based in Geneva, Switzerland. I bring hands-on experience in machine learning, large-scale data analysis, and explainable AI to turn complex data into actionable insights. I enjoy collaborating with cross-functional teams to build interpretable models and communicate results to both technical and non-technical stakeholders, supporting operational and strategic decision-making in fast-paced environments.

Available to hire

Hello, I’m Maryam Bayat Makou, a Data Scientist based in Geneva, Switzerland. I bring hands-on experience in machine learning, large-scale data analysis, and explainable AI to turn complex data into actionable insights.

I enjoy collaborating with cross-functional teams to build interpretable models and communicate results to both technical and non-technical stakeholders, supporting operational and strategic decision-making in fast-paced environments.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

German

Fluent

Italian

Intermediate

French

Beginner

Persian

Fluent

Work Experience

Researcher (Data Science & Machine Learning) at CERN

January 1, 2025 - Present

Define and implement data-driven solutions across multiple AI and analytics projects, leveraging large-scale, heterogeneous datasets. Perform data cleaning, validation, and structuring to ensure reliability and reproducibility of downstream analysis and models. Conduct feasibility and performance analyses to assess data quality and operational constraints. Develop reusable Python-based analytics tools for evaluation, reporting, and decision support. Apply explainable AI techniques (SHAP and custom methods) to improve model transparency and stakeholder trust. Collaborate with domain experts to align analytical solutions with system and hardware requirements.

Research Assistant (Machine Learning & Analytics) at DESY (Deutsches Elektronen-Synchrotron)

January 1, 2021 - January 1, 2024

Designed and delivered end-to-end data analysis pipelines on large, heterogeneous datasets. Built and optimized supervised machine learning models (XGBoost, neural networks), achieving a two-fold performance improvement through tailored handling of imbalanced data. Diagnosed and improved model behavior using explainable AI techniques, strengthening robustness and interpretability. Translated analytical results into clear, actionable insights for technical and non-technical audiences.

Research Assistant (Master’s Project, Deep Learning) at DESY

January 1, 2019 - January 1, 2021

Developed and optimized deep learning models in Keras/TensorFlow for rare-event classification, with a focus on interpretability via Taylor expansion. Improved model convergence using PCA-based decorrelation and robustness-focused feature preparation.