Muneer Ahmed Email and Phone Number

• Gather financial data from various sources, including stock market data, economic indicators, news sentiment, and more, use AWS data storage solutions (e.g., Amazon S3) for efficient data storage and retrieval.• Preprocess the data with python library i.e. Pandas and NumPy to handle missing values, outliers, and ensure data quality and create a structured dataset for training and testing.• Utilize appropriate machine learning algorithms for predicting investment outcomes including Random Forest, Gradient Boosting, or Support Vector Machines with AWS SageMaker.• Implement deep learning models using TensorFlow/Keras for more complex and accurate predictions also experiment with NLP and LSTM for time-series data.• Develop a user-friendly web interface or dashboard for Wells Fargo analysts to input parameters and view investment predictions.• Deploy models using AWS Lambda, AWS Fargate, or AWS ECS for scalability and reliability.

• Managed software projects with Git, enhancing code quality and collaboration, resulting in a 30% increase in development efficiency for ML projects.• Designed CI/CD pipelines using GitHub Actions, leading to a 50% reduction in deployment time and zero deployment failures over a 12- month period.• Proficient in Python, utilized libraries like Pandas for data wrangling, NumPy for numerical analysis to enhance ML model development and data processing efficiency.• Optimized Python development environments (PyCharm, Jupyter, VS Code), improving ML development.• Developed Scikit-learn, TensorFlow and PyTorch ML models (CNN, RNN, LSTM, GAN, YOLO V8, BERT, RoBERTa, word2vec, etc,.) achieving up to 95% accuracy, tailored to meet specific project needs.• Conceptualized and solved image classification, object detection problems using the Keras Models.• Acquired knowledge of data augmentation, OpenCV and Pillow for real time computer vision.• Guided efforts on natural language processing for text classification, text generation, text processing using NLTK, SPACY, LLM, Transformers.• Utilized Data Zone for robust data governance, ensuring data integrity & reproducibility across all ML model developments.• Mentored team members as trained ML models on AWS Sage Maker, enhancing performance and efficiency, reducing computation time by 40% for complex datasets.• Deployed applications using Docker, achieving 99% consistency across various environments.

• Developed a real-time credit card fraud detection system using Python, scikit-learn, and TensorFlow, integrating logistic regression and random forest algorithms, achieving 95% accuracy.• Managed and processed over 1 million daily transactions, leveraging advanced data preprocessing and feature engineering techniques to reduce financial fraud losses by 70%.• Collaborated with security and compliance teams to align the system with industry regulations, leading to a 20% reduction in false positive rates through regular audits and model refinement.• Facilitated training sessions for the operations team to ensure effective system monitoring and maintenance.• Utilized AWS CloudWatch for real-time performance monitoring and anomaly detection, employing AWS Lambda for serverless computing and S3 for large-scale data management.• Implemented enhanced security protocols using AWS KMS for data encryption and AWS IAM for key control, safeguarding sensitive financial data.