Sameer Raju Khadatkar

Quant AI/ML @ Wells Fargo | M.Tech. (CDS) @ IISc, Bangalore | B.Tech. (Mechanical) @ GCOE, Amravati
📍 Hyderabad, Telangana, India
📧 sameer123khadatkar@gmail.com
🔗 LinkedIn

Summary

I currently serve as a Quantitative Analytics Specialist within Wells Fargo's Model Risk Management (MRM) team at India and Philippines. My primary responsibility involves validating AI/ML models, with a focus on fraud detection, as well as models used in marketing, credit scoring, and natural language processing (NLP). In this role, I ensure the conceptual soundness of models, conduct performance testing, conduct explainability analysis and rigorously challenge models by developing challenger models to detect weaknesses.

Additionally, I ensure compliance with regulatory standards set by Wells Fargo, in alignment with guidelines from the Federal Reserve and the OCC. I work closely with model development and risk management teams, providing validation feedback and recommending improvements. I also contribute to documentation and reporting, preparing validation reports, and ensuring the ongoing monitoring of model performance.

With a strong foundation in Machine Learning, Deep Learning, and High-Performance Computing gained during my graduate studies at the Indian Institute of Science, Bangalore, and a Bachelor's degree in Mechanical Engineering, I bring a unique blend of skills at the intersection of advanced technology and engineering. My expertise allows me to tackle complex challenges, drive innovation, and contribute to cutting-edge solutions in diverse industries.

Professional Experience

Wells Fargo International Solutions Private Ltd

Quantitative Analytics Specialist – AVP
📍 Hyderabad, Telangana, India
📅 August 2022 – September 2023

Collaborating with a team overseeing an inventory of ∼300 models focused on Fraud Detection, primarily utilizing Logistic Regression, Extreme Gradient Boosting (XGBoost), and Neural Network models.
Conduct validation of AI/ML models by ensuring conceptual soundness, performing performance testing, carrying out explainability analysis, and developing surrogate, challenger, and offset models to uncover potential weaknesses.
Joined the team during its expansion in India, playing a key role in building trust with US stakeholders. Recognized with the Manager’s Spotlight Award for outstanding dedication and contributions.
Developing a module to assist Validators in benchmarking anomaly detection models (Isolation Forest, Extended Isolation Forest, Autoencoders, Histogram-Based Outlier Score (HBOS), etc.) and assessing them using clustering performance metrics.
Created a validation playbook for fraud detection vendor models and developed an Excel-based policy library to facilitate quick reference for team members.

Highlighted Projects at Wells Fargo

✅ Check Authorization Model | Validation

Validated a high-impact machine learning model for check authorization, ensuring compliance with regulatory and bank's MRM standards.
Reviewed model objectives, assumptions, architecture, and data pipeline.
Assessed performance using AUC, recall, KS statistic, and PSI across time.
Performed explainability analysis using multicollinearity checks, surrogate models (overall and segment level), SHAP, PDP, H-Statistic, 2D-PDPs, and sensitivity analysis.
Identified local weaknesses through segmentation and built offset models to detect missed signals.
Developed challenger models using YOLOv5, SigNet, TrOCR (Transformer-based OCR), XGBoost model, and pixel-based feature engineering.

🧠 Word Embedding Explainability Research

Collaborated with the Bank’s Chief Model Risk Officer on a research project focused on the explainability of word embeddings using clustering techniques such as Spectral Clustering, HDBSCAN, and analysis of ReLU neural network activation patterns.
Utilized Sentence Transformer embeddings (SBERT) and applied dimensionality reduction methods including PCA, UMAP, and t-SNE for cluster interpretation and visualization.
Extended the research by developing a Mixture of Experts model leveraging XGBoost.

Education

Indian Institute of Science (IISc), Bangalore
📅 2020 – 2022
🎓 Master of Technology (M.Tech.), Computational and Data Sciences
📍 Bengaluru, Karnataka
CGPA: 9.1 / 10.0

Government College of Engineering, Amravati (GCoEA)
📅 2015 – 2019
🎓 Bachelor of Technology (B.Tech.), Mechanical Engineering
📍 Amravati, Maharashtra
CGPA: 8.29 / 10.0

Certifications

Advanced Data Science with IBM (Coursera)
HYPERMESH (SHELL MESH AND SOLID MESH)
Introduction to Big Data (Coursera)
MASTERCAM (Design, Turning and Milling)
CREO PARAMETRIC

Research Publication

Subspace Recursive Fermi-Operator Expansion Strategies for Large-Scale DFT Eigenvalue Problems on HPC Architectures
📝 Sameer Khadatkar, Phani Motamarri (MATRIX Lab) 📅 July 20, 2023
📚 Journal of Chemical Physics, 159, 031102 (2023)
🔗 Publication Link

Implemented recursive Fermi-operator expansion methods on multi-node CPU (PARAM Pravega) and GPU (ORNL Summit) systems for large-scale DFT problems.
Applied mixed-precision strategies achieving 2× to 4× speedup over diagonalization.
Benchmarked using MPI and SLATE for distributed dense linear algebra.

Academic, Independent and Other Projects

LLM-Powered Multimodal Airline Chatbot: Built a chatbot with GPT-4o-mini, supporting both text and voice, generating pop-art city images. Stack: Python, Gradio, custom tools.
Future Stock Price Prediction for MAANG: Used yfinance, Stateful LSTM vs XGBoost. LSTM outperformed with ~0.02 MAE.
Duplicate Question Detection: LSTM Siamese Network with Word2Vec and GloVe. GloVe performed better.
Music Genre Classification: Used MFCCs and spectral features. Best result: 76% ± 3% accuracy with SVM.
Algorithm Implementation from Scratch: PCA, LDA, GMM, TF-IDF, and backpropagation for DNNs.

Skills

Knowledge Areas:
Model Risk Management, Machine Learning, Deep Learning, High-Performance Computing

Programming Languages:
Python, C, C++ (OpenMP, MPI, CUDA), SQL

Python Libraries & Tools:
Numpy, Pandas, Scikit-Learn, PyTorch, TensorFlow (Keras), PySpark, Matplotlib

Relevant Courses

Machine Learning for Signal Processing (IISc)
Advanced Data Science with IBM (Coursera)
Deep Learning (NPTEL)
Pattern Recognition and Neural Networks (NPTEL)
Numerical Linear Algebra (IISc)
Data Analysis and Visualization (IISc)
Numerical Solution of Differential Equations (IISc)
Parallel Programming (IISc)
Introduction to Big Data (Coursera)
LLM Engineering: Master AI, Large Language Models & Agents (Udemy)

Extracurricular Activities

Project Associate at MATRIX Lab, CDS Department, IISc.
Teaching Assistant for “DS284: Numerical Linear Algebra” at IISc.
Led suspension operations for SAE BAJA Team at GCoE Amravati.
Organized Annual Social Gathering as Joint Secretary at GCoE Amravati.

Top Skills

Data Reporting
SQL
Microsoft Excel

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