Abhinav Patel

2+ years of work experience

Oct 20241 yr 4 mosJan 2026

Remote

Worked on Financial Market Trend Analysis for Investment Strategy, collaborating with investment analysts, product managers, and the finance team in Agile sprints. Conducted requirement-gathering sessions to define the scope and objectives of the analysis.  Utilized SQL for advanced querying, including window functions and CTEs, to extract, filter, and aggregate data from large financial datasets. Optimized ETL pipelines using AWS tools like AWS Athena, processing data from market feeds in CSV format.  Applied Time-Series Analysis to analyze market trends, volatility, and price movements over the past 5 years, identifying correlations between key market indicators (e.g., stock indices, commodity prices) and investment returns, leading to actionable insights.  Leveraged Python (NumPy, Pandas) for data preprocessing and transformation. Used Moving Averages and Exponential Smoothing to identify long-term trends, reducing noise in the market data and improving forecasting accuracy by 12%.  Performed data validation using AWS services like AWS Glue to ensure the integrity of financial data during the ETL process. Cleaned and normalized over 10 million rows of market data, achieving 98% data accuracy.  Developed interactive Power BI dashboards showcasing financial market trends. Incorporated features like drill-down analysis, trend forecasting, and interactive filtering using DAX to provide real-time insights, improving strategic decision-making for investment managers by 18%.

Jan 20221 yr 8 mosAug 2023

Mumbai, Maharashtra, India

Collected and organized customer transactions and demographic data from various sources like the bank’s CRM and transactional database. Collaborated with marketing, product, and IT teams via Agile sprints to define requirements for specified banking services.  Utilized SQL and Azure Data Factory to optimize database queries, streamline ETL processes, and manage data extraction from sources like customer profiles and transaction logs, improving data pipeline efficiency by 25%.  Applied k-means clustering and RFM (Recency, Frequency, Monetary) analysisto segment the customer base into distinct groups. This led to the identification of 5 core customer segments, driving targeted campaigns that improved conversion rates by 15%.  Used Python libraries like NumPy and Pandas for data cleaning, transformation, and feature extraction, ensuring accurate analysis of 500,000+ customer records. This improved data integrity and helped in segmentation accuracy by 20%.  Leveraged R for performing PCA to reduce dimensionality in customer data, identifying the key drivers of customer behavior across 3 primary segments. This enabled more targeted marketing strategies and boosted customer engagement by 12%.  Validated the data quality through Azure Data Validation services and automated ETL pipelines using Azure Synapse Analytics, ensuring 98% data accuracy and completeness, which significantly reduced manual effort in data preprocessing.  Developed interactive Tableau dashboards showcasing customer segments and their behavior patterns. Used Level of Detail (LOD) expressionsfor advanced filtering, which provided granular insights, helping the marketing team increase their campaigns’ ROI by 20%