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Hi, I’m Harika Paamu, a Data Scientist with around 4 years of experience delivering ML, NLP, and Generative AI solutions in finance, BFSI, and retail. I enjoy turning complex data into actionable insights and building production-grade models and pipelines using Python, SQL, and modern ML frameworks to solve real-world бизнес problems. I thrive in cross-functional teams, delivering interpretable models, interactive dashboards, and end-to-end MLOps across AWS, GCP, and Azure. I’ve led risk, fraud, and anomaly detection initiatives on millions of records, and I’m passionate about bringing advanced analytics into production to optimize operations and reduce risk.…Hi, I’m Harika Paamu, a Data Scientist with around 4 years of experience delivering ML, NLP, and Generative AI solutions in finance, BFSI, and retail. I enjoy turning complex data into actionable insights and building production-grade models and pipelines using Python, SQL, and modern ML frameworks to solve real-world бизнес problems. I thrive in cross-functional teams, delivering interpretable models, interactive dashboards, and end-to-end MLOps across AWS, GCP, and Azure. I’ve led risk, fraud, and anomaly detection initiatives on millions of records, and I’m passionate about bringing advanced analytics into production to optimize operations and reduce risk.

Harika Paamu

Data Scientist, AI Engineer, Data Analyst, +1

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Hi, I’m Harika Paamu, a Data Scientist with around 4 years of experience delivering ML, NLP, and Generative AI solutions in finance, BFSI, and retail. I enjoy turning complex data into actionable insights and building production-grade models and pipelines using Python, SQL, and modern ML frameworks to solve real-world бизнес problems. I thrive in cross-functional teams, delivering interpretable models, interactive dashboards, and end-to-end MLOps across AWS, GCP, and Azure. I’ve led risk, fraud, and anomaly detection initiatives on millions of records, and I’m passionate about bringing advanced analytics into production to optimize operations and reduce risk.…Hi, I’m Harika Paamu, a Data Scientist with around 4 years of experience delivering ML, NLP, and Generative AI solutions in finance, BFSI, and retail. I enjoy turning complex data into actionable insights and building production-grade models and pipelines using Python, SQL, and modern ML frameworks to solve real-world бизнес problems. I thrive in cross-functional teams, delivering interpretable models, interactive dashboards, and end-to-end MLOps across AWS, GCP, and Azure. I’ve led risk, fraud, and anomaly detection initiatives on millions of records, and I’m passionate about bringing advanced analytics into production to optimize operations and reduce risk.

Available to hire

Hi, I’m Harika Paamu, a Data Scientist with around 4 years of experience delivering ML, NLP, and Generative AI solutions in finance, BFSI, and retail. I enjoy turning complex data into actionable insights and building production-grade models and pipelines using Python, SQL, and modern ML frameworks to solve real-world бизнес problems.

I thrive in cross-functional teams, delivering interpretable models, interactive dashboards, and end-to-end MLOps across AWS, GCP, and Azure. I’ve led risk, fraud, and anomaly detection initiatives on millions of records, and I’m passionate about bringing advanced analytics into production to optimize operations and reduce risk.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Data Scientist at JPMorgan Chase

December 1, 2024 - Present

Built production-grade ML pipelines using Python, Scikit-learn, XGBoost, and NumPy for credit risk scoring and probability-of-default modeling across 10M+ retail banking accounts, improving risk assessment accuracy by 25% and reducing loan defaults. Developed NLP-driven intelligence models with BERT and spaCy to analyze customer support tickets, dispute logs, and transaction notes, cutting review and resolution time from 6 hours to under 2 hours while surfacing high-risk behavior patterns. Implemented semantic search and RAG frameworks with Pinecone vector databases, indexing 500K+ historical transactions to enable sub-500ms retrieval for compliance and audit investigations, doubling analyst efficiency. Applied gradient boosting and ensemble techniques to detect anomalous transactions and emerging risk trends, achieving ROC-AUC of 0.91. Created LLM-assisted analytical workflows with LangChain and GPT architectures, enabling analysts to query structured and unstructured banking data nat

Data Scientist at Hexaware Technologies

December 1, 2020 - July 1, 2023

Constructed large-scale predictive models using Python, Scikit-learn, XGBoost, and Pandas to analyze 5M+ financial transactions for BFSI clients, improving fraud detection precision by 28% and reducing false positives by 18%. Engineered time-series forecasting pipelines using Prophet and LSTM to predict customer demand and revenue trends across multi-region retail clients, reducing forecast variance by 22% and optimizing inventory planning cycles. Configured NLP-driven analytics solutions using Transformers and spaCy to process 1M+ customer interaction records, extracting sentiment and intent signals that improved customer retention strategies by 15%. Designed real-time anomaly detection frameworks using Spark, FastAPI, and AWS Lambda to monitor transaction irregularities, achieving sub-minute alert latency across distributed enterprise systems. Architected ensemble-based risk scoring models with LightGBM and Random Forest, integrating structured and semi-structured datasets from Snowf