Dec 202412 min
Quantum Machine Learning in Finance
Exploring hybrid quantum-classical models for credit risk assessment and their performance improvements over traditional methods.
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SUMMARY / 2026
Amol
Deshmukh
Data Scientist with 7+ years of industry experience specializing in machine learning, AI, and quantum algorithms within the financial sector.
PhD in high-energy nuclear physics.
New York, NY
CURRENTLY
Computational Scientist
@ IBM
Oct 2020 — Present
FOCUS
PythonMachine LearningPyTorchScikit-learnQiskit
Experience
2014 — 2026
2020 — Present
Computational Scientist
IBM
Prototyped hybrid quantum-classical ML models for credit default prediction, achieving 41 basis point improvement over XGBoost baseline. Collaborated with PayPal and American Express on quantum optimization algorithms.
PythonQuantum ComputingML/AIPyTorch
2018 — 2020
Data Scientist
EXL
Built propensity models using stacked ensemble techniques for credit card marketing campaigns. Implemented advanced clustering algorithms to improve customer segmentation and profitability analysis.
PythonMachine LearningPySparkCustomer Analytics
2014 — 2018
Graduate Research Assistant
The Graduate Center, CUNY
Investigated quantum properties of baryon systems using effective field theory. Implemented Metropolis algorithm in Python and C for thermodynamic property calculations.
PythonCNumerical AnalysisStatistical Modeling
Research & Insights
Nov 20248 min
Advanced Customer Segmentation
Techniques for improving clustering algorithms to maximize profit separation and predictive accuracy in financial services.
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Oct 202410 min
Ensemble Methods at Scale
Building robust propensity models using stacked ensemble techniques for large-scale financial applications.
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Sep 202415 min
Quantum Computing Applications
Understanding the practical applications of quantum algorithms in solving business optimization problems.
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Let's Connect
Always interested in new opportunities, collaborations, and conversations about AI, machine learning, and quantum computing.