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Data Scientist with 3+ years in Data Analytics, Machine Learning & Fraud Analysis
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Assessing your cultural and operational fit
Highly skilled Data Science professional with 3.2 years of experience spanning data analytics, machine learning, and financial risk management. Proven ability to lead operational activities, analyze complex data, and implement data-driven process improvements. Expertise in developing machine learning models for credit risk prediction and deep learning solutions for medical image analysis, demonstrating a strong foundation in Python, SQL, and various ML frameworks. Committed to leveraging analytical insights for strategic decision-making and operational efficiency.
COVENTRY UNIVERSITY
MSc Data Science · Data Science
January 1, 2022 – January 1, 2023
KRCT
B.Tech Electronics & Communication Engineering · Electronics & Communication Engineering
September 1, 2017 – September 1, 2021
Royal Mail
Deputy Manager
September 1, 2024 – Present
India
TSYS EMEA
Account Research Specialist | Fraud Analyst
April 1, 2023 – September 1, 2024
India
Cancer Cell Detection Using Deep Learning
June 1, 2026 – Present
Built a CNN-based model (EfficientNet-B7) to classify cancerous cells from microscopic images. Conducted data preprocessing, augmentation, and hyperparameter tuning using TensorFlow and Keras. Benchmarked against VGG16 and ResNet50, achieving superior accuracy, precision, recall, and F1-score. Proposed a scalable framework for integration into healthcare diagnostic systems. Tools & Technologies: Python | TensorFlow | Keras | OpenCV | Matplotlib | Seaborn
Credit Risk Prediction Model
June 1, 2026 – Present
Developed machine learning models (Logistic Regression, Random Forest, XGBoost) to predict loan default risk. Performed feature engineering, handled imbalanced data using SMOTE, and optimized model performance. Evaluated results using ROC-AUC and precision-recall metrics. Demonstrated relevance to credit risk assessment, financial decision-making, and banking analytics. Tools & Technologies: Python | Pandas | NumPy | Scikit-learn | Matplotlib | Seaborn
Cultural Fit Analysis
The candidate's project diversity, spanning credit risk prediction and cancer cell detection, shows a broad interest and adaptability in applying data science across different domains. The experience in financial risk management and operational leadership suggests a pragmatic, results-oriented approach. While the current role at Royal Mail is less directly technical, the transferable skills in data analysis, KPI monitoring, and process improvement indicate a proactive mindset. The academic background and project work align well with a data-driven culture, though the professional experience is not purely in data science.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical thinking, problem-solving, and decision-making skills through project descriptions and work experience. Experience in stakeholder management, team collaboration, and communication is evident from managing customer interactions and coordinating operational activities. The Royal Mail role highlights leadership and workflow optimization, while the TSYS EMEA role emphasizes regulatory compliance and risk mitigation, all valuable for operational fit in a senior data science role.