Quantitative Analyst

2 weeks ago

Chennai, Tamil Nadu, India Quantaleap Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Immediate opening for "Quant Analyst AI/ML, Risk Analytics & Payment Processing"

Job Title: Quant Analyst AI/ML, Risk Analytics & Payment Processing

Job Location: Chennai (Hybrid Model)

Experience Required: 5 10 Years

Job Requirement:

  • Develop and maintain Financial Crime and AML Models aligned to regulatory frameworks and organizational model governance requirements.
  • Design and implement Machine Learning models for transaction monitoring, suspicious pattern detection, and fraud risk mitigation.
  • Build fuzzy matching algorithms and entity resolution logic to enhance sanctions screening accuracy and reduce false positives.
  • Perform customer segmentation using clustering techniques such as K-Means, RFM analysis to drive targeted engagement.
  • Develop predictive models for customer behavior and product adoption using supervised ML techniques.
  • Conduct feature engineering, statistical analysis, performance tuning, and model validation in alignment with compliance controls.
  • Analyze transactional and behavioral datasets to provide data-driven insights supporting business and risk strategy.
  • Prepare model documentation, validation reports, and audit-ready analytics summaries.
  • Collaborate with Compliance, Digital Banking, and Technology teams to implement analytics solutions in production environments.

Job Responsibilities:

  • Maintain operational viability of deployed AML models ensuring stable performance and minimal false positives.
  • Lead end-to-end project delivery across data extraction, model development, testing, implementation, and monitoring.
  • Ensure regulatory compliance and alignment with model risk governance standards.
  • Communicate technical recommendations to non-technical business stakeholders effectively.

Desired Qualifications:

Domain Expertise

  • Experience in Financial Crime / AML / Sanctions Screening / Fraud Risk Analytics.
  • Familiarity with AML platforms, fuzzy match engines, and risk scoring frameworks.

Technical Skills

  • Strong expertise in Python, SQL, Scikit-learn, XGBoost, Random Forest, and statistical modelling.
  • Skilled in data preparation, feature engineering, and handling large datasets.
  • Knowledge of visualization/reporting tools: Matplotlib, Tableau, Excel.

Soft Skills

  • Strong analytical thinking, documentation, and stakeholder communication.
  • Ability to balance detection efficiency with operational practicality.

Interested can share their updated resume to

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