Divya Ramanujachari
Development
Tamilnadu, India
Skills
Data Science
About
Divya Ramanujachari's skills align with System Developers and Analysts (Information and Communication Technology). Divya also has skills associated with Database Specialists (Information and Communication Technology). Divya Ramanujachari appears to be a low-to-mid level candidate, with 5 years of experience.
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Work Experience
Lead Data Analyst / Consultant, Mad Street
Customer Analytics
June 2023 - March 2024
- Own customer lifecycle from acquisition to advocacy - main point-of-contact for the • • biggest customer of 2023 in terms of revenue and number of brands From acquisition perspective, assist presales in analyzing the potential uplift that • • could be provided by using vue's offerings During the onboarding phase, perform catalog and historical transactional data • • analysis to identify optimal recommendation strategies and define custom metrics as per customer requirements As part of advocacy, monitor customer performance and continually determine • • opportunities for improvement/upsell Product Development Identify which custom client requirements would be a good add to the platform and • • come up with a roadmap to incorporate it Participate in proof of concept programs from the perspective of expanding the • • existing range of strategies
Data Scientist
Credit Suisse
May 2019 - February 2023
- Feed Monitoring Dashboard - Anomaly Detection Built a dashboard for monitoring ingestion and transformation feeds • • Enabled early detection of anomalous runtime and run volumes in 75% of the cases, • leading to faster incident remediation Automated Data Quality Management Analyzed historical data quality issues reported with a view to automating the • • resolution process Designed an ensemble of classifiers to achieve this by integrating with an existing • • ETL pipeline that onboards data into the warehouse, eliminating the need for manual review in 70% of the cases LIBOR Transition - Document Analysis Examined contracts to identify and modify LIBOR clauses and evaluated the • • possibility of automated keyword-value extraction Decreased manual reviewing efforts by 80%, resulting in savings of $3M by using • • GCP's Document AI and MS Azure Form Recognizer
Education
Virginia Tech
MS Computer Engineering
January 2015 - January 2018
