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Data Science with less than a year in Machine Learning, Deep Learning, and AI-integrated systems.
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Fresh graduate in Computer Science and Engineering with a major in Data Science from East West University. Passionate about machine learning, deep learning, and data-driven solutions. Published researcher with hands-on experience building AI-integrated systems and big data applications. Eager to apply academic expertise and research skills in a professional environment.
East West University
Bachelor of Science in Computer Science & Engineering (CSE) · Data Science
August 1, 2021 – June 30, 2026
Central Women's College, Tikatuli
Higher Secondary Certificate (HSC) · Science Group
June 1, 2018 – May 31, 2019
Feni Government Girls' High School
Secondary School Certificate (SSC) · Science Group
June 1, 2016 – May 31, 2017
Explainable Random Forest Framework for Real-Time Indoor Air-Quality Prediction
January 1, 2025 – December 31, 2025
Developed a machine learning framework using an Explainable Random Forest model to predict real-time indoor air quality at airports using SCD30 sensor data. Applied Explainable AI (XAI) techniques to improve model interpretability and transparency. Successfully presented at the 2025 IEEE International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN 2025), Bangladesh, 2025. [IEEE Xplore: doi.org/10.1109/QPAIN65869.2025.11171710], held at Bangladesh Army University of Science and Technology (BAUST), Saidpur, Rangpur, Bangladesh (July 31 - August 2, 2025). Organized by IEEE Photonics Society Bangladesh Chapter.
AgriFreshNET: AI Integrated System for Detecting Freshness and Predicting Shelf-Life of Product
January 1, 2025 – December 31, 2026
Designed and implemented an AI-based system to detect the freshness of agricultural products and predict its remaining shelf life using deep learning techniques. Integrated computer vision models to classify products condition from image data, enabling real-world agricultural applications. Conducted extensive model training, evaluation, and performance.
Food Recipe Search using FAISS Algorithm
January 1, 2025 – December 31, 2025
Built a semantic food recipe search engine leveraging the FAISS (Facebook AI Similarity Search) algorithm for fast approximate nearest-neighbour search over large recipe datasets. Implemented vector embeddings for recipe ingredients and instructions to enable similarity-based retrieval at scale. Demonstrated efficiency of FAISS indexing for big data search applications compared to traditional keyword-based methods.
View ProjectPower BI Data Modelling Basics Tutorial Course
Microsoft
January 1, 2026 – Present
Explainable Random Forest Framework for Real-Time Indoor Air-Quality Prediction
IEEE QPAIN
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects show a strong interest in applying AI to diverse problems, from agriculture to air quality and information retrieval. The publication at an IEEE conference indicates a drive for knowledge sharing and engagement with the scientific community. The focus on Explainable AI aligns with ethical AI development, which can be a positive cultural fit for organizations prioritizing transparency. However, the candidate is a fresh graduate with no professional experience, which limits the assessment of cultural fit in a professional work environment.
Soft Skills & Operational Fit
The candidate demonstrates soft skills such as team collaboration, problem-solving, and research & critical thinking through project descriptions and listed skills. The academic project work, including a published paper, suggests a proactive and research-oriented approach. However, the lack of professional experience means operational fit in a corporate environment is yet to be proven.