Jan 2024 – Jun 2024 · 5 mos · Gurugram, Haryana, India

• Spearheaded the design and development of Advanced Bug Reporting Tool using React.js, Tailwind CSS, Material UI, JavaScript, and TypeScript for the frontend, and GraphQL and Node.js for backend interactions. Revamped entire Sprinklr’s bug-tracking cycle
• Boosted entire user feedback precision by 50% by implementing advanced features like screenshot commenting, drawing utilities, and video capture to boost user interaction and significantly optimize the bug-tracking process.

Dec 2023 – Aug 2024 · 8 mos · Mumbai, Maharashtra, India · Remote

• Analyzed diverse datasets using statistical and machine learning techniques to identify statistical arbitrage opportunities in US, European, and Asian equity markets on WorldQuant Websim
• Developed 50+ equity long/short strategies using Python and proprietary in-house programming language, achieving Median Sharpe ratio of -2.1, median annualized returns - 7.2%, median maximum drawdown - 1.5%

May 2023 – Aug 2023 · 3 mos · Toronto, Ontario, Canada

• I was awarded the MITACS Global Research Internship / Scholarship for fully funded research in Canada during Summer 2023 at Toronto Metropolitan University, Ontario, Canada.

May 2023 – Aug 2023 · 3 mos · Toronto, Ontario, Canada · On-site

• Played a pivotal role in the design and coding of the Lung Ultrasound GUI Application, utilizing Python to automate the detection of lung ultrasound patterns.
• Translated medical concepts into a user-friendly software application, utilizing HTML & CSS to enhance the user interface for doctors & clinicians.
• Worked with state-of-the-art models & Deep Learning Algorithms for real-time pattern recognition for detecting lung ultrasound patterns.

Mar 2022 – Jun 2022 · 3 mos · New Delhi, Delhi, India

• ■Research Project on Stress Detection Using Machine Learning and Physiological Signal
• Performed signal processing on AffectiveROAD Dataset involving data preprocessing, noise removal and feature extraction on PPG (or BVP) derived- HRV and Heart Rate, GSR ( or EDA), Physiological Signals and Skin Temperature.
• Trained and performed hyperparameter tuning on XGBoost, Random Forest, and KNN machine learning model to predict stress for binary and three class (baseline vs. medium stress vs. high stress) stress level classification involving Pearson correlation feature selection method.
• Performed a Detailed analysis and evaluated accuracy, F1 score, AUC, and ROC Curve for each signal and all combinations. Achieved the best accuracy of 80.8 %- for all combined signals, which is better than the previous work.
• ■ Research Study on Stress Recognition in academic students.
• Using Empatica E4 smartwatch to collect real-time physiological signals in response to stressful situations in academic students of 40+ DTU Students by using programmed Montreal Imaging Stress Task (MIST) to simulate three stress situations while their Facial expressions are recorded by the camera.

Dec 2021 – Feb 2022 · 2 mos

• Expanded backend functionalities using C# (C-Sharp) and ASP.NET framework through AGILE methodology, which increased portal efficiency by 20% and ensured scalability and efficiency in software development.
• Conceptualized and created database schema using ADO.NET and MS SQL with data management, transformation, validation, and security, resulting in a 25% reduction in data errors.
• Optimized and refactored front-end pages using HTML, CSS, JavaScript, resulting in a 50% decrease in errors and improved performance, while enhancing user experience and increasing portal usage

Jan 2021 – Apr 2021 · 3 mos

• Performed Data preparation and cleaning for the past 20 years of flood data in India on an International Project.
• Used Python, Pandas, Matplotlib, and Seaborn to perform Data Analysis, Visualization, and statistical modeling.