Sreejita Mazumder

Early career bioinformatics professional with hands-on experience in developing and validating machine learning models on health data. Strong foundation in study design, algorithm validation, and cross-functional collaboration. Proficient in Python, TensorFlow, and Scikit-learn, with a keen interest in applying AI and data analytics to healthcare challenges. Actively seeking a data scientist or data analyst role where I can contribute to innovative and safe clinical AI solutions through rigorous validation, stakeholder engagement, and a solid understanding of medical standards and regulations.

• Time Series Forecasting with LSTM – Personal Project January 2025

 Designed and validated bidirectional LSTM models using TensorFlow to capture temporal dependencies in time-series data. Applied model evaludation techniques to improve prediction  accuracy, demonstrating robust validation and model assessment skills.

• ML Engineer Recruitment Challenge – Draft Beer Image Classification February 2025

Developed and validated convolutional neural network (CNN) models using TensorFlow and the VGG16 architecture for image classification tasks. Demonstrated proficiency in handling unstructured image data, model evaluation, and delivering results within time-constrained environments.

• Colorectal Cancer Classification – Vrije Universiteit Amsterdam February 2023 – March 2023

Developed and validated machine learning and deep learning models (Naive Bayes, SVM, MLP) on TCGA clinical data. Performed exploratory data analysis (EDA), feature engineering, and feature selection to improve model performance.

Breast Cancer Subtype Classification – Vrije Universiteit Amsterdam April 2022 – May 2022

Developed and validated machine learning classifiers (SVM, Logistic Regression, K-NN) to classify breast cancer subtypes using genomic array CGH data. Applied feature selection and dimensionality reduction techniques (UMAP) to improve model performance, demonstrating strong data analysis and model validation skills.

• Data and AI Ethics Foundations (Udemy, April 2025)

Data Ethics, AI Ethics, Responsible AI, Data Privacy.

• Statistics for Data Analysis using R (Udemy, April 2025)

Descriptive Statistics, Inferential Statistics, Probability Theory, Hypothesis Testing, Data Visualization, Regression Analysis, Data Analysis, Data Cleaning and Preprocessing.