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+1 940-344-0084 EMAIL AVAILABLE LINKEDIN LINK AVAILABLE https://github.com/Xaaby EducationThe University of Texas at Dallas Aug 2023 May 2025 Master of Science in Information Technology and Management Dallas, USA Thakur College of Engineering and Technology Jul 2016 Jun 2020 Bachelor of Engineering in Mechanical Mumbai, India ExperienceInfosys Pvt Ltd India Jan 2022 Jul 2023Analyst Pune, India Engineered predictive models using machine learning algorithms like Random Forest and Support Vector Machine, for customer behavior analysis, achieving a 15% churn reduction with targeted retention strategies. Developed a real-time analytics dashboard employing data visualization tools like Matplotlib, Seaborn, and Plotly to monitor and enhance customer experience, leading to a 30% reduction in repeat customer calls. Led and optimized ETL processes for a Hadoop and Spark-based Big Data warehousing solution, enhancing data flow and reducing data retrieval times, which improved overall data processing for analytics by 40%. Enacted Principal Component Analysis (PCA) for feature extraction in high-dimensional datasets, optimizing model training time by 25% and improving predictive accuracy for customer segmentation tasks. Used statistical methods including Correlation Analysis, and conducted k-means clustering for exploratory data analysis, uncovering customer satisfaction trends which led to a 20% reduction in complaints. Carlton Industrial Engineer India Oct 2020 Oct 2021 Data Quality Analyst Thane, India Collaborated with cross-functional teams to identify automation and enhancement opportunities, resulting in a Percentage of time saved in reconciliation after implementation from 40% in 1st year to 85 % by the 3rd year. Data analysis of complex data sets using SQL & Python. Optimized lead-to-account mapping ratios by Identifying data integrity discrepancies & process improvement opportunities with actionable insight about customer & market. No of employees working in finance dropped by 40% in the span of 3 years. Innovated automation helping the finance team conduct data checks and cleansing of the finance data received and processed. In total, 10920 hours were eliminated by cash collection over 2 years while cost savings from productivity in payroll posting in 5 years crossed over 1 million dollars. Designed interactive dashboards using Tableau to represent program metrics gathered from business requirements analysis and data mining activities. Assessed and reported the efficiency of firm initiatives to drive insights. ProjectsProf. Sun Customer Clustering in a Supermarket Spearheaded the development of Prof. Sun, a customer clustering analysis tool, which provided insights into 20% increase in targeted market effectiveness and a 15% improvement in sales conversion rate for multiple supermarkets. Utilized advanced clustering algorithms and data viz techniques to analyze customer demographics and purchasing behavior, resulting in a 30% reduction in customer acquisition costs and a 25% increase in customer retention rates. Advocated data-driven marketing strategies based on insights from Prof. Sun, leading to a 40% increase in sales aimed for customer segments and a 20% improvement in inventory turnover rates. IBM HR Attrition Data Analytics and Visualization Executed Principal Component Analysis (PCA) to perform dimensionality reduction and visualized significant dimensions through an interactive scree plot, providing the capability to choose intrinsic dimensionality selectively. Utilized K-means clustering for data segmentation, utilizing the elbow method to visually determine the optimal number of clusters, enhancing data analysis with optimally clustered visualizations. Produced Multidimensional Scaling (MDS) plots to visualize data points and variables based on Euclidean distance &(1 - correlation) metrics, integrating clustering results for comprehensive data insights. Technical SkillsMethodologies: SDLC, Agile, Waterfall.Languages: Python, R, SQL, PL/SQL, SAS, Unix, PySpark, Java. IDEs: Visual Studio Code, PyCharm, AWS Sagemaker.Machine Learning & Statistical Techniques: CNN, RNN, LSTM, LangChain, LLMs, Deep Neural Networks, Linear Regression, Logistic Regression, Decision Trees, Classification, SVM, Random Forests, Naive Bayes, KNN, K Means, Hypothesis Testing, Time Series Forecasting, Supply Chain, Generative Models. ML-Ops: AWS EC2, Lambda, Aurora, DynamoDB, IAM, Elastic Beanstalk, RDS, AWS S3, Glacier, SQS, SNS, Cloud Formation, Route53, VPC, Cloud Watch, Docker, Databricks. Packages/Tools: NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, Seaborn, TensorFlow, PySpark, Spacy, MS Excel. Visualization Tools: Tableau, Power BI, Alteryx, Matplotlib, Seaborn, Plotly. Databases: MS SQL Server, MySQL, Oracle, MongoDB. |
