Professional Summary
Data scientist in Fraud and AML analytics, focused on
end-to-end model development from data extraction and EDA
to model deployment and monitoring.
Hands-on experience with XGBoost-based fraud models, AML/FCC analytics,
feature engineering, hyperparameter tuning, and optimization of precision-recall
and business outcomes.
Hands-on experience with supervised (Logistic Regression, Random Forest, XGBoost)
and unsupervised machine learning algorithms (K-Means, GMM).
Experience
NICE Actimize - Data Scientist, Fraud Detection (Apr 2026 - Present)
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Model Development:
Building XGBoost-based fraud detection models across the full lifecycle:
data extraction, EDA, data preparation, feature selection
(correlation analysis and lift analysis), model training,
precision-recall based evaluation, hyperparameter tuning,
and optimization of business metrics including detection rate,
alert rate, and value detection rate.
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AI Initiative:
Enhanced the idea submission platform with workflow automation,
including email notifications using SES and SQS and secure SSO login.
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Dashboard Development:
Building dashboards for model monitoring in an AWS environment
to track model and operational performance.
Solytics Partners - Quantitative Consultant (July 2023 - March 2026)
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Customer Risk Rating System:
Designed and deployed end-to-end customer risk rating models for
10,000+ customers using rule-based and ML frameworks.
Built demographic and transaction-based behavioural features,
performed variable selection, trained models
(Logistic Regression, Random Forest, XGBoost, CatBoost),
and implemented SHAP-based explainability for regulatory compliance.
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Customer Segmentation:
Developed unsupervised clustering models (K-Means, GMM)
to identify transaction-behaviour-based customer segments.
Built interactive compliance dashboards using Streamlit and Plotly.
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Model Validation:
Validated Customer Risk Rating, TMS alert prioritization,
and segmentation models covering:
conceptual soundness, data validation,
model performance (recall, precision, discriminatory power),
stability (PSI, CSI), production code review,
explainability (SHAP, LIME, PDP, counterfactuals),
and performance monitoring. Also validated 5+ onboarding channels
and performed TMS ETL validation on 30M+ transactions,
identifying critical data inconsistencies.
R&D Projects
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Adverse Media Screening:
Designed LLM prompts for adverse media screening,
reducing false positives by ~30%
through contextual understanding.
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Anomaly Detection Dashboard:
Built an AML anomaly detection framework using
Isolation Forest and One-Class SVM with
behavioural feature engineering and
LLM-generated investigative narratives.
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Graph Network-Based Fraud Detection:
Developed graph-based AML models using
network metrics and community detection
to identify money laundering networks,
supported by LLM-based explanations.
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RAG-Powered CBUAE Guidelines Chatbot:
Built a compliance chatbot using
Retrieval-Augmented Generation (RAG),
vector embeddings, and Guardrails AI.
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LLM-Based Investigation Application:
Developed an investigation assistant using
Gemini LLM function-calling to dynamically
extract customer, account, and transaction details.
Technical Skills
Fraud Analytics / AML / FCC
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Customer Segmentation, Customer Risk Rating,
Alert Prioritization (TMS & Name Screening),
Fraud Detection Modeling,
Transaction Behavioural Analytics,
Suspicious Activity Detection,
KYC/AML Compliance,
Model Risk Management,
Threshold Tuning
Machine Learning for AML/FCC
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Classification: Logistic Regression, Random Forest,
XGBoost, CatBoost
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Anomaly Detection: Isolation Forest, One-Class SVM
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Clustering: K-Means, GMM, DBSCAN
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Graph Analytics, Feature Engineering,
SHAP/LIME Explainability,
ROC-AUC, PR-AUC
Programming & Tools
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Python, R, SQL, Pandas, scikit-learn,
XGBoost, CatBoost, MLflow,
Streamlit, Power BI,
AWS (SageMaker, Lambda, ECR, App Runner, SES, SQS),
Plotly, FastAPI
Generative AI
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RAG Architecture, Vector Embeddings,
Prompt Engineering, Agentic AI
Education
M.Sc. in Statistics
Shivaji University, Kolhapur (2021 – 2023)
CGPA: 9.5 / 10
Key Coursework:
Linear Algebra, Statistical Inference,
Regression Analysis, Optimisation,
Data Mining, Time Series Analysis,
Design of Experiments,
Generalized Linear Models,
Multivariate Analysis