Experienced QA Engineer with 2+ years in software testing in the security domain, shifting focus to Data Science and Machine Learning. Proficient in Python (NumPy, Pandas, Seaborn, Matplotlib, Keras, Scikit-Learn, TensorFlow), SQL, and data visualization with Tableau. Strong analytical skills and experience in Linux. B.Tech in Electronics and Communication Engineering.
Lead Scoring for E-commerce Customer Segmentation
Developed a machine learning model to score and segment e-commerce customers based on behavior and conversion likelihood. Conducted data preprocessing, feature engineering, and applied logistic regression algorithms. Optimized the model using cross-validation and hyperparameter tuning, achieving improved accuracy and actionable insights for targeted marketing. Tools used include Python, Scikit-Learn, and visualization libraries for analysis and reporting.
Spark Jobs Migration from EMR to EKS
As part of infra testing team conducted thorough testing for the migration of Spark jobs from Amazon EMR to Amazon EKS, ensuring seamless transition and performance consistency. Validated job execution, data processing, data accuracy, processing speed and system reliability. Collaborated with development teams to identify and resolve issues, optimizing performance and resource allocation within EKS. This testing ensured robust functionality, minimized downtime, and contributed to the project’s successful migration and deployment.
SOAR
SOAR refers to technologies that enable organizations to collect inputs monitored by the security operations team. For example, alerts from the SIEM system and other security technologies- where incident analysis and triage can be performed by leveraging a combination of human and machine power- help define, prioritize and drive standardized incident response activities. SOAR tools allow an organization to define incident analysis and response procedures in a digital workflow format.