Senior Quantitative Analyst in Financial Analytics

Padmanand Nambiar

Analytics professional who develops innovative data-driven

solutions to improve pricing, risk, and portfolio decision-making

**********@*****.*** +1-706-***-**** Linkedin

Phoenix, Arizona

SUMMARY

Senior Quantitative Analyst with 15+ years of experience in data analysis, statistical modeling, and predictive pricing strategy for financial services. Expert in SAS, SQL, Snowflake, and Databricks with a proven ability to translate complex analytics into business impact. Intermediate experience in Python. PhD in Applied Economics with 7+ years of research experience. Certified in Stanford Machine Learning. Actively adapting advanced ML modeling techniques to risk frameworks, fraud analytics, and anti-money laundering (AML) methodologies. EXPERIENCE

J.P. Morgan Chase Senior Quantitative Analyst Oct 2016 – Aug 2025

Plano, TX

•Owned end-to-end lifecycle for 2 Auto Finance pricing models governing $60B+ in annual loan volume; led refreshes delivering improvement in pricing accuracy

•Passed 5+ Model Risk Governance reviews with zero adverse findings; authored SR 11-7 compliant documentation for BASEL III

•Built automated daily Excel dashboard monitoring pricing changes, reducing manual QC time and flagging mis-pricings

•Leveraged LLMs for SQL query generation and ad hoc data pulls, cutting analysis turnaround time

•Extracted/analyzed datasets from Oracle, Teradata, Snowflake, and Databricks using SAS, SQL, Python to support pricing and forecasting

•Led platform migration to Snowflake/Databricks, validating large amount of historical data with 99.9% accuracy

•Trained new hires on analytical tools; presented findings to VP/ED/MD leadership influencing strategy for $100B portfolio

•Collaborated in projects across different teams, including Risk Management

Prescio Analytics & Financial Technology Quantitative Analyst Intern May – Oct 2016 Casa Grande, AZ

•Validated/redeveloped LCR and deposit models for mid-size banks using SAS, ensuring SR 11-7 regulatory compliance

•Prepared BASEL III regulatory documentation for client delivery, contributing to on-time audit submissions

Grand Canyon University Adjunct Faculty – Statistics Jan – Oct 2016 Phoenix, AZ

•Taught 3 concurrent online Business Statistics courses to 100+ students, earning 4.7/5.0 avg faculty rating

•Simplified regression, hypothesis testing, and probability concepts for non-technical business majors

KEY ACHIEVEMENTS

• Governed risk lifecycle of two

Auto Finance pricing models

managing $60B+ annually; passed

5+ SR 11-7/Basel III reviews with

zero adverse findings.

• Led $100B portfolio migration to

Snowflake and Databricks,

validating historical datasets with

99.9% accuracy using SAS, SQL,

and Python.

• Developed a Cashing Rate Report

using booking proxies, detecting

segment trends 1+ weeks early to

drive timely pricing corrections.

TECHNICAL SKILLS

Languages/Tools: SAS, SQL, Excel, R,

STATA, Alteryx, Python

Databases/Cloud: Snowflake,

Databricks, Oracle, Teradata, SQL

Developer

Visualization: Tableau (Public), Excel,

PowerPoint

Statistical/ML Methods: Regression,

Time Series, Multivariate Models,

Genetic algorithms, Monte Carlo, LLM

Prompt Engineering for Analytics

Regulatory: SR 11-7, BASEL III, Model

Risk Governance, CCAR/CECL

University of Georgia Graduate Research Assistant Aug 2009 – Jul 2015 Athens, GA

• Applied econometric/statistical models to household survey data from Uganda to develop consumer profiles for policy makers; published 2 papers in international journals

• Developed genetic algorithms in R to identify optimal experimental designs across multiple criteria

• Presented research at 10+ national/regional conferences including National and Southern Agricultural Economics Associations

CERTIFICATIONS

• Supervised Machine Learning: Regression and Classification (Deep Learning AI. Stanford University)

https://coursera.org/verify/O85LECDHXAD3

• Advance Learning Algorithms (Deep Learning AI. Stanford University) https://coursera.org/verify/11VE9LY58I6W

• Unsupervised Learning, Recommenders, Reinforcement Learning (Deep Learning AI. Stanford University)

https://coursera.org/verify/PRLBF5PFVY7V

• Machine Learning (Stanford Online)

https://coursera.org/verify/specialization/GFBEXY298W9K

• Excel Skills for Business: Advanced (Online, (Macquarie University, Sydney, Australia)

https://coursera.org/share/9d589435176cad61909316a4d3adaed2

• Prompt Engineering for ChatGPT

https://www.coursera.org/account/accomplishments/certificate/QZJ1A73L1GAZ

• SQL for Data Science (Online, US Davis, Coursera) https://www.coursera.org/account/accomplishments/certificate/AIF17CUDBJ3W

• Ace Salesforce Certified Tableau Desktop Foundations Exam https://www.coursera.org/account/accomplishments/verify/QOSFXQ10GYQC SELECT PUBLICATIONS

• Madhavan-Nambiar, P., et al. 2016. Attitudes of urban female consumers toward food production practices in Korea. J. Central European Green Innovation, 4, 83-96.

• Madhavan Nambiar, P., et al. 2015. Measuring vulnerability of urban Korean women to weight management problems. Food and Nutrition Sciences, 6, 1496- 1506.

AWARDS

Claudia de Palma Davis Outstanding MS Student Award, UGA, 2010 Best Poster Award, Global Educational Forum, 2011 Winner, SAEA Logo Design Competition, 2011

LEADERSHIP POSITIONS HELD

• President, Graduate Student Association, Department of Ag. and Applied Economics (2011-2012)

• Vice-President, Graduate Student Association, Department of Ag. and Applied Economics (2010-2011)

•Student Representative, Department Graduate Committee (2011, 2012, 2013) TECHNICAL INTERESTS

• Quantitative Decision Science,

Machine Learning, Credit Risk,

Pricing & Optimization,

Portfolio Analytics, and

Profitability Modeling, with

applications across Auto

Finance, Credit Cards, and

Corporate Banking.

• Applying Stanford ML

coursework to simulate

anomaly detection and

predictive modeling

frameworks in Python.

• Independent study of financial

crime patterns, focusing on

how unsupervised learning

(e.g., clustering, isolation

forests) identifies

transactional risk.

CONTINUOUS LEARNING

& UPSKILLING

• Committed to modernizing

quantitative workflows

through targeted technical

specializations.

• Formally trained in ChatGPT

prompt design to optimize

data cleaning workflows and

automate script generation.

• Integrating generative AI tools

to accelerate Python and SQL

code development.

EDUCATION

University of Georgia,

Athens, GA

• Ph.D., Applied Economics,

Jul 2015

• M.S., Statistics, May 2015

• M.S., Applied Economics,

Aug 2010

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