Results-driven professional with a Master's degree in Management Information Systems from Northeastern University and a Bachelor's degree in Computer Science from Mumbai University. Adept in leveraging a comprehensive skill set encompassing programming languages (Python, C, R), databases (Oracle SQL, MySQL, NoSQL), and data analysis and visualization tools (Power BI, Tableau, TensorFlow). Proven track record in software development, data engineering, and implementing machine learning models. Skilled in ETL processes, data warehousing, and cloud technologies (AWS, Azure, GCP). Recognized for optimizing workflows, enhancing website performance, and collaborating with cross-functional teams to scale operations. Extensive experience in diverse projects, including predictive modeling, system enhancements, and data-driven decision-making. Committed to delivering innovative solutions in data science and analytics.
• Engineered automated expense reporting and reconciliation pipelines using Python and SQL, streamlining financial workflows across 6+ consular departments and reducing manual processing time by 40%.
• Built interactive Tableau dashboards analyzing $2.3M+ in consular expenditures by integrating SQL and Excel data sources, enabling leadership to make fiscal decisions 3x faster.
• Performed end-to-end data cleaning, validation, and preprocessing on 50,000+ financial records using Python (Pandas) and Excel, reducing inconsistencies by 35% and improving reporting accuracy to 98%+.
• Streamlined cellular assay data analysis using Databricks, implementing k-means clustering and logistic regression, reducing processing time by 20% and delivering actionable insights faster.
• Led A/B testing projects to optimize customer engagement strategies, resulting in a 5% increase in key metrics such as click-through rate, conversion rate, and user engagement.
• Analyzed Flow, Nucleic acid, and T1D assay data using Python and SQL, conducting exploratory data analysis (EDA) to improve scientist comprehension by 40%, enabling more effective decision-making.
• Developed dynamic Power BI dashboards and integrated 4 data sources with SQLAlchemy, enabling real-time tracking of 15+ KPIs and improving decision-making efficiency for 10+ stakeholders.
• Designed and automated ETL pipelines using Python and AWS Redshift, processing 8M+ records and saving 100+ hours of manual work monthly, boosting operational efficiency by 50%.
• Optimized SQL workflows for analytics dashboards, reducing cloud storage costs by $10K annually and enhancing reporting efficiency by 30%.
• Conducted exploratory data analysis (EDA) on search filter usage across two distinct search experiences using SQL and Excel, optimizing filter functionality, reducing site latency by 10%, and improving overall user experience.
• Optimized SQL queries to enhance the efficiency of Looker Studio dashboards, processing 25+ reports more efficiently, improving performance, and reducing server resource usage and cloud storage costs by $10,000 annually.
• Streamlined dashboard workflows in Looker Studio by automating data retrieval and integration processes, improving data accessibility and reducing manual efforts by 30%.
• Leveraged Excel tools such as Pivot Tables and VLOOKUP to analyze large datasets, enabling actionable insights and improving reporting accuracy across key business metrics.
• Delivered lectures on "INFO 7374 - Applied Machine Learning using Python in Finance", simplifying complex concepts in machine learning, quantitative finance, and Python programming for enhanced student comprehension.
• Guided hands-on projects in topics such as statistical analysis, machine learning algorithms, and data analytics, equipping students with essential skills for practical applications in finance and data analysis.
• Facilitated in-depth discussions on advanced topics, including predictive modeling, financial forecasting, and Python-based data processing, fostering critical thinking and problem-solving among students.
• Mentored students in applying machine learning techniques to real-world finance scenarios, enhancing their technical proficiency and career readiness in the data-driven finance industry.
Skills: Python (Programming Language) · Machine Learning · Data Analytics · Financial Analysis · Predictive Modeling · Data Processing · Problem Solving in Finance · Applied Mathematics · Financial Forecasting · Statistical Data Analysis
• Analyzed eBay Global Shipping data using Python, SQL, and PySpark-driven ETL solutions, delivering data-driven insights that improved operational processes and contributed $100K in additional monthly revenue.
• Performed A/B test analysis on 10+ KPIs, including add-to-cart rate and conversion rate, using Google Analytics, uncovering insights that drove a 4% increase in sales conversions and enhanced customer engagement.
• Enhanced data retrieval processes with advanced SQL techniques such as joins, CTEs, and window functions, streamlining data preparation for 50+ reports and saving 10+ hours per week in manual processing.
• Visualized insights using Tableau, creating dashboards that effectively communicated key trends and operational metrics to stakeholders for data-driven decision-making.
• Optimized data workflows with Airflow, automating report generation and reducing processing time by 30 minutes per report, significantly improving operational efficiency.
• Implemented scalable ETL pipelines using PySpark, ensuring efficient data processing and improving data quality across eBay’s global shipping datasets.
• Collaborated with cross-functional teams to analyze data using SQL views, presenting findings that informed strategic decisions and enhanced operational performance.
• Analyzed 10,000+ shipments by major carriers like UPS etc. using SQL queries to provide insights into sales performance.
• Utilized Power BI and Tableau to analyze data on active users, purchases resulting in 5% improvement in the system model.
• Collaborated with a team of 10 shipping experts in a fast-paced environment to implement changes based on the insights gained using the analyzed data, resulting in a 20% increase in shipping efficiency and an impressive 15% decrease in shipping costs.
Relevant Coursework: Application Engineering & Development, Database Management & Data Design, Data Science Engineering Methods and Tools, Applied Machine Learning and Python in Finance
Relevant Coursework: Sequence of Applied Mathematics, Discrete Mathematics, Object Oriented Programming, Data Structures, RDBMS, Machine Learning, Natural Language Processing, Python, Theory of Computer Science and Communications Skills.
CGPI: 8.05/10
• Developed and optimized a machine learning model, achieving an impressive 89.9% accuracy for hair loss prediction.
• Streamlined data science lifecycle by reducing data processing time by 10% through effective data preparation techniques.
• Emphasized hyperparameter tuning, yielding a 15% query efficiency boost and model refinement for superior performance.
• Demonstrated expertise in model interpretation techniques like SHAP, LIME, and PDP, enhancing transparency, trust, and actionable insights, driving a 10% improvement in query efficiency.