Customer Churn Prediction in Retail Banking using ANN
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Summary
Developed a supervised classification model to predict customer churn in a retail banking dataset.
About
Results-driven Data Science professional with 2 years of experience, specializing in developing impactful AI/ML solutions, GenAI applications, and automation across aerospace and finance. Expert in predictive modeling, RAG chatbots, and data automation, adept at translating complex business problems into data-driven strategies. Recognized for combining strong analytical acumen with technical expertise to drive significant business outcomes and operational efficiencies.
Work
Accenture
|AI/ML Computational Science Senior Analyst
Bengaluru, Karnataka, India
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Summary
Led the development and deployment of AI/ML solutions, including predictive models and RAG chatbots, to drive significant operational efficiencies and enhance decision-making for aerospace and finance clients.
Highlights
Developed an XGBoost regression model to accurately forecast total revenue from long-term aerospace engine maintenance contracts, achieving an R² of approximately 0.75.
Enabled early-stage revenue visibility for multi-million-dollar contracts, significantly improving pricing accuracy and commercial planning decisions.
Triggered contract-level revenue deviation alerts through model insights, prompting timely reviews of underperforming agreements and earning the 'Star of the Business' award.
Developed 25+ financial data validation reports from scratch for a leading aircraft engine manufacturer, ensuring contract revenue accuracy through daily client engagement and KPI establishment.
Managed a 4-5 analyst reporting team across 25+ sub-services, maintaining zero client escalations and ensuring smooth delivery across multiple cycles.
Engaged with client stakeholders to onboard and stabilize maintenance planning activity, cutting Turnaround Time (TAT) by 50% through Python-based automation.
Automated manual services, saving over 3600 minutes per year with 100% accuracy, and delivered Power BI dashboards central to client governance workflows.
Developed a Retrieval-Augmented Generation (RAG) chatbot using LangChain, Hugging Face embeddings, FAISS, and Groq's LLaMA3 API, enabling natural language querying of complex semi-structured and unstructured contracts.
Education
Madras School of Economics
Chennai, Tamil Nadu, India
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M.A.
Financial Economics
Grade: 9.3/10
Dyal Singh College, University of Delhi
Delhi, Delhi, India
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B.A. (H)
Economics
Grade: 8/10
DL DAV Model School
Shalimar Bagh, Delhi, India
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AISSCE (Class XII)
Commerce Math
Grade: 92%
DL DAV Model School
Shalimar Bagh, Delhi, India
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AISSE (Class X)
Class X
Grade: 9.2/10
Awards
Star of the Business Award
Awarded By
Accenture
Awarded 4 times in 2 years for significant contributions in client delight and operational rigor, demonstrating exceptional performance and commitment.
Gold Medalist in Masters in Financial Economics
Awarded By
Madras School of Economics
Recognized for academic excellence and strong analytical skills, achieving the highest distinction in the Master's program.
Skills
Analytical Skills
Data Science, Machine Learning, Deep Learning, Natural Language Processing (NLP), Statistics, Predictive Modeling, Data-driven Strategies, Problem-solving.
Technical Skills
Python, Pandas, NumPy, Scikit-Learn, Matplotlib, SQL, LangChain, TensorFlow, Keras, Advanced Excel, Power BI, XGBoost, Retrieval-Augmented Generation (RAG), Hugging Face Embeddings, FAISS Vector DB, Groq's LLaMA3, ANN, Feature Engineering.
Projects
Stock Recommender AI Agent
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Summary
Created a multi-agent financial assistant designed to generate real-time stock insights and investment recommendations.