Mahdi Zakizadeh

Data Scientist

https://talent.gravityer.com/mahdi-zakizadeh

NLP Researcher

Member since June 25, 2026

Key Strengths

Demonstrated strong interest and practical experience in Natural Language Processing (NLP) and Machine Learning (ML) through multiple personal projects.
Experience with analyzing gender bias in language models and developing mitigation techniques, indicating a focus on ethical AI and fairness.
Proficiency in Python, a core language for Data Science, evidenced by numerous project implementations.
Familiarity with research-oriented projects (e.g., EMNLP publication related to DiFair), suggesting an ability to engage with academic and cutting-edge topics.
Exposure to various data science related tools and concepts like RAG systems (KARA project) and dependency tree pruning.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's project portfolio shows a strong inclination towards research and development in NLP and ML, particularly in areas like bias detection and mitigation. This aligns well with roles that value innovation, ethical considerations in AI, and continuous learning. The diversity of projects, including a mix of academic-leaning work and practical implementations, suggests adaptability and a broad technical curiosity. However, without information on team-based projects or professional experience, assessing cultural fit beyond technical alignment is limited.

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. The candidate's project descriptions are clear but do not provide insights into collaboration, problem-solving approaches, or communication style in a team setting.

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Projects

kara

July 5, 2025 – Present

KARA is a Python toolkit for efficient document updates in RAG systems.

View Project

Gender-Encoding-Patterns

March 9, 2025 – May 2, 2025

This repository provides replication code for analyzing gender bias encoding patterns within pretrained language models and evaluating the effectiveness of various bias mitigation techniques.

View Project

gender_bias_benchs_diverse_perspectives

January 2, 2025 – January 2, 2025

gender_bias_benchs_diverse_perspectives — GitHub repository

View Project

difair_public

October 18, 2023 – December 31, 2023

This repository provides the evaluation code and a generated dataset for DiFair, an EMNLP 2023 publication. DiFair presents a nuanced benchmark for assessing gender bias and knowledge retention in pre-trained language models, introducing the Gender Invariance Score (GIS) as a unified metric.

View Project

ml01

September 18, 2022 – January 13, 2023

TeIAS Machine Learning Course - Fall 1401

View Project

nlp00

January 25, 2022 – June 11, 2022

nlp00 — GitHub repository

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DTP

October 18, 2021 – November 2, 2021

Dependency Tree Pruning for Retaining Information in WIC Tasks

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pizza_simplified

October 17, 2020 – October 23, 2020

Implementation of Pizza Simplified UI concept by Yaroslav Zubko

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shifting_tabbar

March 25, 2019 – July 12, 2025

A custom tab bar widget for Flutter framework.

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LSMTree.jl

February 13, 2019 – October 9, 2019

Log-structured merge tree implementation with Julia

View Project

Key Strengths

Demonstrated strong interest and practical experience in Natural Language Processing (NLP) and Machine Learning (ML) through multiple personal projects.
Experience with analyzing gender bias in language models and developing mitigation techniques, indicating a focus on ethical AI and fairness.
Proficiency in Python, a core language for Data Science, evidenced by numerous project implementations.
Familiarity with research-oriented projects (e.g., EMNLP publication related to DiFair), suggesting an ability to engage with academic and cutting-edge topics.
Exposure to various data science related tools and concepts like RAG systems (KARA project) and dependency tree pruning.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Mahdi Zakizadeh

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit