Mayesha Tasnim

Data Scientist

https://talent.gravityer.com/mayesha-tasnim

PhD student at Civic AI Lab, University of Amsterdam

Amsterdam, North Holland, Netherlands

Key Strengths

Demonstrated experience with Python and Jupyter Notebook for data-related projects.
Exposure to various data science and machine learning concepts through project descriptions (e.g., Generative Flow Networks, strategic modeling).
Familiarity with big data tools like PySpark and Pandas (via bd22-docker-setup project description).

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects show a strong academic/research focus, particularly in areas like mechanism design, strategic modeling, and knowledge graphs. While these demonstrate analytical capabilities, the direct alignment with a typical industry 'Data Scientist' role requires further validation. The diversity of technologies (e.g., Scala, JavaScript, HTML alongside Python) indicates a broad technical curiosity, but also suggests a potential lack of deep specialization in core data science tools.

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.

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Evaluating your skill match against the job requirements…

Assessing your cultural and operational fit

Projects

school-choice-survey

June 6, 2026 – Present

Code for paper "Modeling Strategic Risk in School Choice: A Case for Transparent Design"

View Project

strategic-rank-minimization

June 6, 2026 – Present

Code repository for the paper Strategic Manipulation of Preference in the Rank Minimization Mechanism

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regulated-strategic-lending

February 25, 2026 – Present

Codebase for the working paper "The Role of Regulatory Institutions in Strategic Lending"

View Project

gfn-matcher

November 10, 2024 – Present

Code repository for generating preference-based agent-item matches using Generative Flow Networks

View Project

transport-network-school-choice

November 11, 2022 – March 7, 2025

transport-network-school-choice — GitHub repository

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bd22-docker-setup

December 7, 2021 – February 3, 2022

Setup of the Jupyter Notebook environment including PySpark, Pandas and DuckDB for course Big Data 2022

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temporalsummary

September 11, 2019 – September 18, 2019

Entity Temporal Evolution Summarization

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StandardOntologyVisualization

January 11, 2018 – September 13, 2019

StandardOntology Visualization in D3

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I40KG

March 24, 2017 – November 17, 2021

Contains the development for the Industry 4.0 standards knowledge graph (I40KG). Its current collaborative development is driven by VoCol - http://vocol.iais.fraunhofer.de/sto/

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FuhSen-reactive

December 18, 2015 – September 24, 2018

Reactive version of the Federated RDF-Based Hybrid Search Engine

View Project

Key Strengths

Demonstrated experience with Python and Jupyter Notebook for data-related projects.
Exposure to various data science and machine learning concepts through project descriptions (e.g., Generative Flow Networks, strategic modeling).
Familiarity with big data tools like PySpark and Pandas (via bd22-docker-setup project description).

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.

Mayesha Tasnim

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit