Jaimin Patel

Architected and implemented scalable, cloud-based data pipelines on AWS to support real-time analytics, reducing ETL processing time by 50%. Partnered with CXOs and senior leadership to define data strategies, enhancing data integration, reporting, and decision-making capabilities. Led PoCs on tools such as Coalesce, Honeydew, Omni, and Vertex AI, improving data processing speed by 30%. Integrated Vertex AI and ML solutions to develop predictive analytics models, improving forecasting accuracy and operational efficiency. Automated data governance and reporting workflows, saving 3 reducing report runtimes by 60%. Integrated Vertex AI and other ML tools to develop predictive analytics solutions, improving forecasting accuracy and operational efficiency. Automated data governance, quality checks, and reporting workflows, reducing manual effort by 3 hours/day/resource and optimizing report runtimes by 60% through intelligent analytics solutions.

Partnered with senior management to deliver actionable insights, driving informed decisions for acquirers and merchant banks. Developed and deployed scalable data pipelines using PySpark and Shell scripting, ensuring efficient data storage on HDFS. Created comprehensive design documentation (HLD, LLD) and conducted unit/A/B testing for robust solution delivery. Automated ML model workflows using Airflow, improving efficiency and data processing reliability. Delivered critical insights through Tableau dashboards, empowering data-driven business strategies. Established CI/CD pipelines, enhancing deployment speed, system reliability, and scalability. Collaborated with global teams to analyze regional data patterns, improving model accuracy and insights. Designed a high-performance ML model for fraud detection, reducing risks for financial institutions. Developed and optimized models using Decision Trees, Gradient Boosting, and SVM, improving model precision through hyperparameter tuning. Integrated advanced AI techniques like RAG and Transformer architecture with Kubernetes and Docker, achieving transaction speeds of 0.3 milliseconds.

Build and maintain optimal data pipeline architecture, assemble large, sophisticated data sets that meet functional / non-functional business requirements as a part of Merger and Acquisition Implemented statistical modeling and process control to enhance data-driven decision-making processes for budgeting and forecasting which enhanced the accuracy from 78% to 96%, enabling the port managers to run the operations smoothly. Communicated complex technical concepts to non-technical stakeholders, including C-suite, facilitation the understanding and buy-in for data driven insights. Spearheaded the design and implementation of advanced analytical models, resulting in efficient budgeting with improvement of 90% of allocation of funds and 55% improvement in revenue leakage identification. Oversaw data governance initiatives to ensure data accuracy, consistency, and compliance with regulatory standards. With the implementation of time series model for budgeting and forecasting, there was an improvement in the allocation of funds to the respective port managers by 90%, enabling continuity of business and minimal disruption. Gathered requirement from various stakeholders including the CTO and CFO to optimize the data solutions and improvise the existing flows. Migrated the entire finance application to Azure cloud and optimized the rum time from 7 days to 26 hours for month end reports.

Developed custom data analytics solutions to address specific client needs and challenges. Developed predictive prepayment probability Model, to identify if the customer will pay back the loan prior to the tenure or default. This model reduced the score generation time from 6 months (using MATLAB and multiple user inputs) to 2 days (using R and fully automated) and enhanced the model accuracy to 98%. Presented findings and recommendations to various stakeholders including key decision makers to drive informed decision making. Collaborated cross-functionally with stakeholders to identify business requirements and deliver actionable insights. Analyzed large data sets to identify trends, patterns, and opportunities for process optimization. Created a compilation of encryption and decryption algorithms suitable for safeguarding banks data and comparing them with BASEL data used in regulatory reporting.

Responsible for many business-critical reports and data. Timely and accurate delivery of data to end users. Performed requirement gathering, current-state analysis, data mining and in-depth root cause analysis of several business process and over 100 TB of enterprise data across entire Telecommunication life cycle. Built and deployed Telecom churn prediction model to identify potential customer and revenue loss. We linked it to various factors related to call logs, any associations, and corporates. This enabled the client to enable the marketing team with potential customers and offers. Performed POC based on SMS to see how likely a customer is to read a message and aiding potential marketing strategies. Furnished data-driven insights by developing a PoC on Classification and Sentiment Analysis of unstructured customer feedback data using ML and NLP and churn prediction.