• Designed and led the Gen AI projects within Capital One. Used Meta’sLlama and Mixtral model for Earnings call classification and sensitive data tagging projects.
• Led the work for setting coding and testing standards across multiple teams within my Line of Business.

• Designed and led the Gen AI projects within Capital One. Used Meta’sLlama and Mixtral model to enhance the operational efficiency of internal processes.
• Worked on Prompt Engineering (Few shot, Chain of Thought, etc), Agentic workflows for Meta’s Llama and Mixtral model.
• Spearheaded the development of a comprehensive data pipeline for an MBS deep learning model using PySpark, Python, and Polars. Successfully extracted and processed terabytes of data from external vendors,implementing intricate business logic to support model training, scoring, and ongoing monitoring.
• Created a novel Segmentation Tree, a data explainability tool. Presented the tool at Capital One’s Modeling and Analytics Conference (MAC) and DataWeek, being among the 20% accepted from 500+ submissions company-wide.
• Reduced the MBS model’s data pipeline runtime by 96%. This was achieved by code and process optimization.
• Implemented data validation pipelines using data profiler to increase consumer confidence for the data.
• Implemented model monitoring controls to make sure model inputs and outputs meet all the audit and compliance requirements.

• Developed data quality checks for LCR usecase to ensure accuracy and consistency of data using SQL and Python.
• Designing a machine learning framework to predict data quality issues for High priority usecases.
• Created a Data Quality framework with anomaly detection and change point detection capabilities.

Client: Global Leader in Water, Hygiene, and Energy Technologies & Services(Pest activity risk score prediction) Tools: Python, PowerBI

• Conducted exploratory data analytics to identify patterns of pest activity observed during service visits across locations.
• Designed and predicted custom prevention score by applying LightGBM predictive model to identify key changes to be made at a facility by thecustomer, leading to higher customer retention.

Client: Leading hospitality and entertainment business in the Midwest (Cohort classification and causal inference analysis) Tools: Python, R, and Tableau

• Classified top revenue-generating customer categories and promotions to drive trip decisions by leveraging techniques such as regression discontinuity and propensity score matching.
• Performed exploratory data analysis using Python and Tableau on 3 years of promotional data.

Client: Mall of America (Exploratory Analytics) Tools: Python, R, and Tableau

• Formulated 4 actionable recommendations by inspecting call data tooptimize its workforce allocation.
• Evaluated relationship between different categories of shops and the call types and identified 3+ recommendations.
• Visualized call data using Tableau to identify trends and identified 10+ insights.

Client: America Based Chemical Company – Offshore Project: A $540M project comprising 500 consultants across 4 continents. Assisted client in transforming their IT landscape, after a $130B merger, for spin-off into three companies.

• Led a team of 3 SRM consultants to deliver critical SAP objects and configurations; acting as an intermediary between client’s business team and internal development teams.
• Reduced process time by 240 hours per quarter through automation of processes involved in importing vendor catalogues into MDM through Accenture proprietary RPA (Robotic Process Automation) tool.
• Saved 300 hours and maintained compliance by analyzing 22K contracts using python and advanced excel.
• Initiated and implemented strategy for client’s MDM system, contributing tothe client achieving savings target during the Spin-Off.

Client: America Based Chemical Company – Offshore Project: Process improvement project in production support for SAP modulesby analyzing the incidents that were converted into change requests. ‘Follow the sun’ delivery model was adopted by 80 consultants.

• Reduced audit cost by 100$K per quarter by developing a product pricechange tracker to help client sourcing team and auditors.
• Presented Development Life Cycle to Client’s CIO and IT Head, which wasappreciated by Project Manager.
• Defined, recorded and presented metrics to senior leadership for optimizingmonthly project plan focused on team productivity.

Client: QATAR Based Oil and Gas Company – Offshore Project: $5M Greenfield transformation project comprising a team of 40consultants from 4 countries. Partnered with the client in migrating it’s IT infrastructure from Oracle to SAP in 10 months.

• Transitioned SRM and SLC module through end-user training sessions,resulting in milestone sign-off to go-live in production.
• Identified as a key resource in succession planning.
• Developed test environment and documentation using user case stories andrequirements.
• Recognised with ACE (Accenture Celebrates Excellence) quarterly award.