Developed an AI-powered credit risk scoring system that utilized both traditional machine learning algorithms (Logistic Regression, XGBoost) and advanced deep learning models to assess loan applicant risk. The system processed over 10 million historical records, and incorporated key financial indicators, credit history, and customer behavior to predict the likelihood of default, reducing default rates by 15%.Led the design and implementation of a real-time fraud detection model for financial transactions, combining anomaly detection (Isolation Forest) and ensemble learning techniques to identify fraudulent activities with 95% precision. Integrated the model into the company's cloud infrastructure using Apache Spark to scale the system across global datasets in real time.Devised a multi-tier recommendation engine for personalized financial products, leveraging both collaborative filtering and deep learning (autoencoders) to offer tailored product suggestions to customers. The model increased customer engagement by 20% and was integrated with the company's CRM system to automate real-time recommendations.Led an advanced customer segmentation project, implementing K-Means clustering and hierarchical clustering on customer purchase behavior and financial data to discover hidden patterns in customer preferences. This segmentation was used to optimize marketing campaigns, resulting in a 25% increase in campaign ROI.Utilized natural language processing (NLP) techniques to analyze and classify unstructured financial text documents. Implemented BERT and LSTM models to categorize legal documents and customer reviews into sentiment-based classes, improving document retrieval time by 40% and providing better insights for product development teams.

Developed a dynamic sales forecasting model using time series analysis (ARIMA, LSTM) for a global retail company. The model predicted future sales trends with over 90% accuracy by analyzing historical sales data, seasonality, and promotional events, helping the company optimize inventory levels and supply chain management. The model scaled across multiple product categories, processing terabytes of transactional data in real-time using Spark and Hadoop.Designed and deployed a real-time recommendation engine for a retail e-commerce platform, utilizing a hybrid approach with collaborative filtering and content-based filtering to suggest products to users based on past behavior and similar customer profiles. Integrated deep learning models using TensorFlow to improve cold-start recommendations, increasing cross-sell and up-sell conversion rates by 18%.Led a predictive maintenance project for IoT-enabled devices in retail stores, where time series sensor data from industrial equipment was analyzed to predict failure points using Random Forest and LSTM models. This resulted in a 30% reduction in equipment downtime, and the system was deployed on Azure for continuous monitoring and real-time alerts.

Built a customer lifetime value (CLV) prediction model using survival analysis techniques and regression-based models, which forecasted the total revenue a customer would generate. This allowed the company to tailor marketing efforts more effectively and improved retention by 12%.Conducted sentiment analysis on social media data to track brand perception and customer satisfaction across multiple products. Developed a pipeline to process and analyze over 1 million tweets, reviews, and posts daily using Python (NLTK, SpaCy) and visualized sentiment trends through dashboards to assist marketing and product teams in decision-making.Collaborated closely with the data engineering team to design and maintain ETL pipelines for the efficient flow of large datasets into a distributed data lake, improving the speed of data processing by 40% and enabling faster insights for stakeholders.

As a Python Developer Trainee, I gained valuable experience working on key projects. I built a RESTful API using Flask, focusing on user account management. I also analyzed data with Pandas and created visuals with Matplotlib.One highlight was developing an inventory management system that helped users track stock and manage orders, connected to a PostgreSQL database. I also created an interactive dashboard with Dash to display sales data, which helped the sales team identify trends.I received positive feedback for my problem-solving skills and had the opportunity to present my dashboard to management, showcasing its insights.