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Ahad Hamirani

@ahadhamirani

AI/ML Engineer with 3 years of applied experience in high-stakes financial environments, specializing in credit risk and fraud detection.

India

Message

What I'm looking for

I’m looking for a role where I can ship ML that improves credit risk and fraud detection in production—bridging research and scalable deployment, owning model integration end-to-end, and collaborating with stakeholders to turn risk signals into better decisions.

AI/ML Engineer with 3 years of applied experience in high-stakes financial environments, specializing in credit risk and fraud detection. Successfully engineered and deployed India’s national bureau credit score, currently utilized by all domestic lending institutions. Currently pursuing an M.S. in Computer Science at Georgia Tech. Expert in bridging the gap between complex ML research (PyTorch, Transformers) and scalable production deployment within the financial services domain.

Experience

Work history, roles, and key accomplishments

Data Modeler II

Experian Credit Services India

Jun 2022 - Feb 2025 (2 years 8 months)

Engineered an updated national bureau credit score that is now live across all Indian credit lending institutions, used as a primary risk signal in lending decisions. Built fraud and acquisition/risk models (including a Mule Risk Indicator and propensity scorecards), managed client integrations for Experian fraud detection scores, and produced credit trend insights; won 1st place in the Experian G

Credit Scoring Fraud Detection Hierarchical Modeling Feature Engineering Client Integration Risk Indicator Development Credit Risk Analytics

Data & Analytics Intern

Jocata Financial and Advisory

Apr 2020 - Nov 2021 (1 year 7 months)

Developed an adverse news media analysis tool using NLP sentiment analysis to identify negative market signals and quantify entity-level financial risk. Built an automated loan operating system analyzing transaction patterns and bank passbooks to support credit decisions, and created graph-based features with rule-based engines to detect fraudulent financial networks.

Sentiment Analysis Entity Level Risk Scoring Graph Feature Engineering Fraud Detection Data Pipelines