Anurag Gupta

Development Lead – Workforce Activity Data Utility (WADU) | JPMorgan Chase (2022–Present)

• Lead the modernization of the legacy EPS (Enterprise Productivity Suite) system, transitioning it to a scalable, cloud-native architecture as part of JPMorgan Chase’s strategic cloud migration initiatives.
• Led a cross-functional team in designing and implementing the WADU platform, enhancing the firm’s capability to collect and analyze workforce activity data for informed decision-making.
• Collaborated closely with stakeholders to ensure the new system met compliance and data privacy standards, aligning with the firm’s regulatory requirements.
• Utilized Infrastructure as Code (IaC) practices to automate deployment processes, improving system reliability and reducing manual intervention.
• Daily Tasks included information gathering, development and testing of various software components.
• Tech Stack : Databricks,AWS (S3, Lambda,AWS Glue), Pyspark, Python, Unix Shell Scripting , API Integration and Testing.

Working as a single point of contact as a Technical Business Analyst for the TCS BaNCS application (DCC EMEA region), day to day tasks included the below but not limited to :

• Build and maintenance of the SIT and UAT environment for Operate testing.
• Providing user support and debugging environmental issues.
• Effectively communicating with the vendor TCS for resolution of technical or functional issues which are beyond the scope the firm.
• Handling production issues
• Requirement gathering ,solution documentation and release preparation.
• Tech Stack: Oracle RDS, PL/SQL, Unix Shell scripting , Jenkins CI/CD pipelines

• Lead developer for Management Information System (MIS) and FBI (Firmwide Business Intelligence)which cater to reporting module , Following were my main roles and responsibilities:
• Single point of contact in the entire project for all business reporting requirements.
• Provided the solutioning for various reporting requirements.
• Requirement Gathering and publishing solution Documents to customers.
• Development,Unit Test Cases Execution and Deployment

• Data Science and Machine learning PGP certification provided by Great Learning India in association with University of Texas at Austin.
• The projects and industry sessions were a great source of practical insights into real-life data science and Machine Learning problems.

Projects :

• Supervised Learning - Personal Loan Campaign Modelling:This case is about a bank (Thera Bank) whose management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors)
• A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over9% success
• This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with a minimal budget
• Ensemble Technique- Problem statement (Term Deposit Sale):Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio
• Feature Selection - Concrete Strength Prediction : To predict the concrete strength using the data available in file concrete_data.xls .Apply feature engineering and model tuning to obtain 80% to 95% of R2score
• Unsupervised Learning - Clustering Project :AllLife Bank wants to focus on its credit card customer base in the next financial year . The bank have been advised by their marketing research team, that the penetration in the market can be improved.Based on this input, the Marketing team proposes to run personalised campaigns to target new customers as well as upsell to existing customers.Another insight from the market research was that the customers perceive the support services of the back poorly.Based on this, the Operations team wants to upgrade the service delivery model, to ensure that customers queries are resolved faster head of Marketing and Head of Delivery both decide to reach out to the Data Science team for help
• Neural Networks - Given a Bank customer, build a neural network based classifier that can determine whether the bank will leave or not in the next 6 months
• Computer Vision - Plant Seedlings Image Classification using CNNs in Keras.pdf
• Natural Language Processing : Twitter US Airline Sentiment : A sentiment analysis job about the problems of each major U.S Airline.Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service")