Rahil Jhaveri

At Nascent, I developed the data platform for the Sovereign desk, focusing on multi-asset datasets from global exchanges. My responsibilities included building data pipelines, integrating data quality checks, and improving workflows to enhance data processing efficiency.

At WEX, I played a pivotal role in the Data as a Service’s Data Engineering team, focusing on optimizing data workflows. I leveraged technologies like Snowflake, DBT, and AWS to migrate ELT workflows, which significantly reduced costs associated with Fivetran-QLIK tooling (saving approx $250k annually). My contributions facilitated the swift onboarding of new clients and their associated data, thereby enhancing operational efficiency.

Worked on research projects in affiliation with the Gulf of Maine Research Institute, United Nations SDG, and ReMo (an edtech startup). Served as an assistant to a professor, writing Python code for class work, evaluating user codes, and advising peers on best practices in respective coursework.

As part of the Applied AI team, I integrated an LLM Evaluator (GPT 4o) within the RAG-based Health Benefits chatbot that reduced development time (by 10hours) while upholding contextual accuracy (82%) for customer responses. I also contributed to the late fee revenue forecasting pipeline for the Fleet Card business, improving the AUC-ROC for defaulters and reducing the MAPE to 12% for a 1-month forward look.

After being promoted to an Associate at J.P. Morgan, I focused on improving the governance of Credit Risk portfolios. I utilized SQL and Python to optimize data processes, developed a robust data pre-processing pipeline for time series data, and led a team in producing insightful reports for executive stakeholders. My contributions significantly streamlined data management and reporting practices within the organization.

As an Analyst in QR Wholesale Credit Risk's Data team, I enhanced the efficiency of quantitative research processes. By productionizing a Python workflow for anomaly detection, I enabled over 15 team members to identify issues across 200+ features in wholesale credit risk portfolios. Additionally, I optimized SQL queries, significantly reducing data extraction time, and partnered with modeling teams to ensure high data quality standards.

In my role as a Sr.DE, I optimized data workflows and enhanced decision-making processes. I architected a firm-wide reconciliation workflow that significantly reduced time spent on manual processes. Additionally, I developed a trade performance pipeline that empowered the investment team to make informed decisions with a daily budget of $100k. My mentorship of junior engineers during a massive data migration project further strengthened our data infrastructure.

At AlphaGrep Securities, as a Data Engineer, I focused on automating data processes and enhancing data extraction efficiency. I utilized Python and Clickhouse to ensure temporal consistency in daily back-filling of price and volume data. My collaboration with cross-functional teams led to significant improvements in data extraction time and resource consumption, while I also managed robust data engineering pipelines to handle large data volumes effectively.

I learned about Data Engineering and implemented a lot of PoCs. One of them was transforming Decathlon’s data reporting capabilities. By migrating their reporting pipeline to AWS using Python, RedShift, and Glue, I successfully reduced costs by 50%. Additionally, I improved the efficiency of their end-of-day Dashboarding pipeline on QuickSight, saving 4 hours compared to their in-house solution.

Back-end Developer CSoD'19 at Crio.Do

Coursework: CS5800: Algorithms DS5110: Data Management & Processing DS5230: Unsupervised ML & Data Mining CS6120: Natural Language Processing DS5220: Supervised Machine Learning & Learning Theory DS5500: Data Science Capstone CS5330: Pattern Recognition and Computer Vision DS7995: Data Science Master’s Research Project

Relevant Courses: Applied Mathematics, Data Structures, Advanced Algorithms, Database Management Systems, Operating Systems, Distributed Computing, Systems Programming & Compiler Construction, Big Data Analytics, Machine Learning, Natural Language Processing, Artificial Intelligence & Soft Computing, Object Oriented Programming