Agastya Raj

Researcher

Dublin, County Dublin, Ireland6 yrs 9 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in developing machine learning models for fraud detection.
Strong background in risk management within financial services.
Researching intelligent systems in optical networks.

Stackforce AI infers this person is a Fintech expert with a strong focus on machine learning and risk management.

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Skills

Core Skills

Risk ManagementMachine LearningData Science

Other Skills

Artificial Neural NetworksComputer VisionContent ManagementData AnalysisData AnalyticsDeep LearningMachine Learning AlgorithmsManagementNatural Language ProcessingOperations ManagementPredictive ModelingPython (Programming Language)

About

Researching intelligent systems in optical networks. Previously worked as a Fraud Risk Analyst at American Express for 3 years. Graduated with a Bachelor’s in Materials Engineering from IIT Roorkee in 2019.

Experience

Connect centre

Researcher

Apr 2022 – Present · 3 yrs 11 mos · Dublin, County Dublin, Ireland · On-site

American express

2 roles

Assistant Manager - Credit and Fraud Risk

Nov 2021 – Apr 2022 · 5 mos

Risk Analyst - Credit and Fraud Risk

Jun 2019 – Oct 2021 · 2 yrs 4 mos

Responsible for managing new accounts fraud controls for American Express products in US market
Developed machine learning based modeling solutions to identify fraudulent credit card applications attempted by lost or stolen identity
In charge of leading innovation and development in modeling techniques to prevent fraudulent applications
Found loopholes in the system and then assessed its potential strength and implemented controls so that the vulnerability of the system can be minimized

Machine LearningData AnalysisRisk Management

Mathlogic

ML Research Intern

May 2018 – Jul 2018 · 2 mos · Gurugram, Haryana, India

Built attention based sequential learning solutions for time series data, improving existing performance by a significant margin.
Implemented and optimised Sequence to Sequence architectures and built a custom pipeline to experiment and develop end to end machine translation tasks in Pytorch.
Experimented with the use of Generative Adversarial Networks in semi-supervised learning, and improved the training of classifiers with insufficient data.

Machine LearningData SciencePython (Programming Language)