Jan 2023 – Jun 2023 · 5 mos · Chicago, Illinois, United States

• Led the development of an anomaly detection model on AWS using deep learning and machine learning to predict
• anomalous behavior of mobile mechanical systems in real-time. The model’s output was integrated with enterprise
• software, generating an average revenue of $10k per system and total $5M/year.
• Developed model performance monitoring scripts using Python & Spark on AWS, re-trained models based on model
• evaluation results and model drift; led to increment in the true positive rate of anomalies detected by 50%.
• Applied statistical transformations and measures to calculate normal channel ranges of mobile IoT sensors emitting time
• series data, which served as model input to multiple ML models, increasing the model’s accuracy by 5%.

May 2022 – Dec 2022 · 7 mos · Chicago, Illinois, United States

Aug 2021 – May 2022 · 9 mos · Chicago, Illinois, United States

• Researched to develop forward-looking feature-based stochastic-dynamic program to learn optimal storage policies for different commodities traded on different platforms using Machine Learning and Optimization
• Assisted two courses, 1. Machine Learning and 2. Operations Management, by taking technical lab sessions, prepare and grade assignments for students and meet students for doubt clarifications

Aug 2019 – Jul 2021 · 1 yr 11 mos · Bangalore

• My responsibility as a Data Scientist was to come up with solutions to solve business problems with the application of my existing skills and if needed by acquiring new skills
• Designed and Deployed a Price Harmonization model leveraging regression and FX pricing techniques, assessing, and improving the efficiency of Bank’s FX pricing grid which led to a 2% increase in FX revenue
• Developed a NLP framework using OCR and text mining techniques to automate the authentication process of insurance claims information by automated scraping, extracting information and thus reducing human error by 60%
• Implemented clustering techniques in PySpark to target groups of retails customers having high FX turnover and segmented the customers based on FX penetration to track profitability which led to YOY USD 3M of revenue lift
• Developed Random Forest and XGBoost models to assess the profitability of a new product and service availability within the existing customer base to extend existing FX products to international customers
• Devised recommendations for servicing new customers leading to 5% increase in FX transacting base for 3 international markets and USD 1.6M incremental FX turnover. Represented analysis in the ‘2020 Global FX SteerCo’ summit
• Led a team of 5 members, working closely with the D&A team, Data Management, and stakeholders in parametrization and integration of multiple dashboards focused on indicative of banking performance and wealth management for 12 strategic business units globally leading to reduction in manual work hours by over 80%

Apr 2019 – Aug 2019 · 4 mos

• Led entire Business Intelligence work stream end to end for America’s leading Investment Banking firm in building business critical dashboards, custom visualizations and conducting trend analysis
• Developed Key Performance Indicators (KPIs) for multiple clients by communicating cross-functionally & visualized the metrics in Tableau for various products for leadership team to take actionable insights
• Designed an OLAP data warehouse from multiple data sources, performed data manipulation and automated the reporting pipeline using Python & SQL, optimizing performance by over 2x
• Developed various product and investment dashboards providing insights which enhanced sales and improved retention, increasing the investment value by 20%
• Built automated dashboards increasing the efficiency, and providing independence to different business units globally leading to significant increase in productivity

Sep 2017 – Mar 2019 · 1 yr 6 mos