Sonali Thote

Developed and implemented an automation and analysis tool for the lab, runs on a cloud server, in a colocation facility – an Assembly Line - which monitors lab tool data. For this Data Monitoring system I was responsible for designing a PostgreSQL-based data monitoring system and integrating Kafka for real-time metrics tracking (e.g., throughput, error rates, and status). Extracted data was cleaned by removing and replacing the missing values, removing outliers and invalid entries. Standardizing the tool names, summarizing data into hourly aggregates, calculating derived metrics like uptime_percentage or error_trend (rolling 24-hour error rate average), resampling data to fixed intervals (5-minute buckets) for dashboard compatibility, and flagging anomalies using thresholds (error_rate → status = "needs_inspection") by integrating with Kafka to trigger alerts in real time. Write this processed data to PostgreSQL for dashboards for analytics. Built RESTful APIs for data visualization and implemented robust database models, enhancing retrieval speed by 30%. This tool improved the labs scalability and performance by 23%.

Working on addressing the "cold-start" problem in containerized applications by meticulously analyzing latency issues in Kubernetes clusters and recently achieved significantly improved performance by optimizing container preload, caching, and eviction based on the machine learning application's request patterns.

Computer Science Engineering with Data Science Specialization. Academic Project: Implemented a system to detect bot requests coming on an auctioning site. Preprocessed, trained 260MB dataset of requests, and tested over 10 different machine-learning models to get a 96% accurate prediction. Also simulated an auctioning site that was bombarded with bot requests to visualize the detected bots on the bidding page. Tech stack used: Python, ML Model Libraries(scikit-learn), Django, HTML, CSS, RESTful API, MongoDB, Postgres DB.

Internship. Implemented a critical feature aimed at optimizing manufacturing assembly line operations by identifying and addressing bottlenecks. Leveraging the assembly station and production details dataset, I designed a system that strategically aggregated data pertaining to processing times. This data served as the cornerstone for pinpointing any production bottlenecks, thereby enhancing overall system efficiency. The aggregated dataset, a pivotal component of our solution, was meticulously crafted and stored on the Databricks cloud platform. Given the high-paced nature of our assembly line, where approximately 1 million records were generated within a mere 24-hour timeframe, this dataset aggregation was a formidable task. It was essential for us to run this batch processing operation every 24 hours to ensure we collected and processed the latest assembly data. In terms of technical implementation, I spearheaded the development of a highly scalable ETL (Extract, Transform, Load) pipeline. This pipeline, crafted using a combination of Scala, Spark, and Databricks technologies, not only processed the data efficiently but also exhibited an exceptional level of robustness and scalability. Furthermore, to ensure the reliability of our codebase, I maintained a rigorous approach to testing, achieving a remarkable 100% code coverage. This initiative bore testament to our commitment to continuously enhance the assembly line's performance. By systematically identifying and addressing bottlenecks using cutting-edge technology and data-driven insights, we made significant strides in streamlining the manufacturing process, ultimately contributing to a notable improvement in the company's assembly line operations.

Architectured and built a highly available and scalable real-time data processing pipeline empowered with real-time machine learning to perform predictive analysis around agricultural data and deploy as well as deliver products and services. Developed the system for Syngenta’s patent on the Runoff and Leaching Algorithm used in agriculture technology to help farmers calculate average fertility loss of soil, building REST APIs for applications consuming this algorithm Served data point processing for ~10 Million farmlands in the European region using LucasDB Generated heatmap of the soil that helped Syngenta improve business investment in Europe by 12% Contributed to the core development process of services that are responsible for predictive and preventive analysis of Syngenta sales for farmers in the USA and all over Europe, depending on data like type of land, total area, location of land, previous & and current year crop produce a percentage. Built a microservice to read geometrical data of the farms and generate an Excel data sheet to feed into Closed-Loop applications used by farmers to automate pesticide spraying around their farms.

Developed a Travel Portal Application for efficient retrieval of business-related travel data, streamlining approval workflows involving multiple stakeholders, and expediting logistical arrangements. Significantly enhanced company-wide travel plan approval rates, reducing latency by approximately 70%. Additionally, designed and implemented an API for the Siam Commercial Bank (SCB) iPlan application, leveraging the Backbase framework. This API empowered bank agents to facilitate the sale of diverse insurance policies to customers, with a utility service that automated PDF generation from backend customer data, including insurance policy details.

Played a pivotal role in enhancing the pricing module of AT&T's enterprise-level application, responsible for generating customer-selected itemized bills and issuing confirmation emails with e-signature requirements. Acquired proficiency in industry-standard programming practices, with a focus on test-driven development (TDD) utilizing Java for backend development and Oracle SQL for database management. Implemented a unified development environment and conducted rigorous unit and integration testing using JUnit and Maven.