• Developed a logistic regression model to identify cases for quick resolution, achieving an 82% accuracy rate; utilized techniques such as feature selection, cross-validation, and hyperparameter tuning to enhance model performance, leading to optimized case handling and significant savings in court resources.• Significantly boosted system throughput and efficiency by applying a random forest model in Python to forecast time-in-court; improved scheduling accuracy and resource allocation through feature importance analysis and model validation, optimizing court operations.• Developed a Scheduling Assistant Dashboard in Power BI to address growing caseload challenges and limited judiciary resources; utilized DAX functions like CALCULATE, SUMX, and FILTER to generate dynamic metrics, and implemented interactive visuals to streamline court scheduling, significantly improving operational efficiency.• Conducted exploratory data analysis (EDA) in Python to identify correlations between variables and assess feature importance, using visualizations and statistical techniques to uncover meaningful patterns and relationships.• Enhanced database performance by normalizing schemas and developing optimized stored procedures in SQL Server using functions like IFNULL, CASE, and CONCAT, improving data integrity, reducing redundancy, and cutting query response times.• Ensured data consistency, accuracy, and reliability by cleaning and manipulating over 10,000 records using Python; applied techniques such as deduplication, handling missing values, and leveraging functions like groupby to prepare high-quality data for in-depth analysis.• Worked closely with stakeholders to gather and understand business requirements, translating them into tailored data analytics solutions using clear communication and iterative feedback.

• Applied advanced Excel techniques such as VLOOKUP, PivotTables, and complex formulas to analyze customer data, uncover insights into preferences, and recommend personalized banking solutions, leading to a 470% achievement of the sales target.• Effectively dealt with complaints and issues from customers by utilizing outstanding communication skills, which led to a discernible rise in customer satisfaction levels.• Collaborated with cross-functional teams to translate complex business requirements into actionable insights, ensuring alignment with organizational objectives and enhancing decision-making processes.

• Transformed raw data into actionable insights by conducting market research and analyzing industry trends using Power BI, enabling the identification of new marketing opportunities and optimizing targeting strategies.• Built an automated ETL pipeline using Python to streamline the marketing team’s manual workflows, utilizing Pyodbc to connect to databases and pandas for data processing and transformation, reducing processing time by 30% and minimizing errors in repetitive tasks.• Improved data accuracy and consistency by 25% through end-to-end data validation and manipulation in Python, applying methods to detect inconsistencies, resolve anomalies, and prepare reliable datasets for analysis.• Optimized data extraction process by developing advanced SQL scripts with CTEs, CASE statements, and window functions to efficiently consolidate over 1 million sales records; reduced manual workload by 10+ hours per week and significantly improved reporting accuracy and processing speed.• Conducted comprehensive exploratory data analysis (EDA) using Python libraries such as pandas, matplotlib, and seaborn, uncovering patterns and trends that informed strategic decision-making while ensuring data quality and consistency.• Weekly client meetings to track project milestones, gather feedback, and ensure alignment with expectations, fostering transparent communication and consistently enhancing the quality of deliverables.

• Performed multi-linear regression analysis using Excel and R to identify key success factors in the hybrid work technologies industry, enhancing client targeting strategies by identifying key growth areas and customer segments.• Automated Power BI reports to boost reporting efficiency, leveraging DAX functions like CALCULATE, FILTER, and SUMX for comprehensive market research on industry trends and competitor performance, enabling data-driven decisions across 11 branch banks.• Integrated and consolidated data from diverse sources such as CSV files, SQL databases, and APIs using Python (Pandas and Pyodbc libraries); performed data cleaning through robust handling of missing values, format standardization, and deduplication to ensure high-quality datasets.• Developed and executed advanced SQL queries to extract, transform, and analyze data from relational databases, delivering accurate and timely insights for business reporting; optimized query performance by 30% through efficient indexing and query tuning techniques.• Delivered actionable insights to stakeholders via clear and concise presentations and reports, translating complex data findings into strategic recommendations that supported informed decision-making.