Risk Analyst

2 weeks ago

Bengaluru, Karnataka, India Vintage Fashions Full time
HIRING FOR AIRBNB

Responsibilities:


1.

Machine Learning Model Development:

  • Develop machine learningbased models for assessing credit risk, fraud risk, and other types of risk, along with associated frameworks.
2.

Credit Risk Strategy Implementation:

  • Implement and monitor credit risk strategies across the lending lifecycle, including acquisitions, underwriting, limit management, crossselling, and collections.
3.

Fraud Risk Control Implementation:

  • Implement and monitor fraud risk controls to safeguard against fraudulent activities.
4.

Alternate Data Analysis:

  • Conduct indepth analysis of alternate data sources and evaluate data features to enhance risk assessment and expand credit opportunities.
5.

Collections Strategy Optimization:

  • Contribute to shaping the omnichannel collections strategy and utilize data analytics to optimize collections efficiency.
6.

Stakeholder Coordination:

  • Actively collaborate with external stakeholders to ensure that relevant insights are effectively utilized for credit decisionmaking processes.
Requirements:

1.

Passion for Data Analysis:

  • Demonstrate a strong passion for working with data and enjoy testing hypotheses using datadriven approaches.
2.

Experience in Retail Unsecured Lending Risk Management:

  • Possess experience in retail unsecured lending risk management, particularly in areas such as credit cards, personal loans, etc.
3.

Machine Learning Skills:

  • Proficient in building machine learning models and scorecards, with the ability to deploy them effectively.
4.

Data Visualization and Analytical Skills:

  • Strong data visualization skills and analytical mindset to extract insights from large datasets and disparate sources of data.
5.

Enthusiasm for Risk Framework Design:

  • Show enthusiasm for designing and implementing a risk framework tailored for a digital bank environment.
6.

Adaptability to New Technologies:

  • Keenness to leverage new technologies and data tools to deliver robust risk controls, design informative risk dashboards, and stay updated with industry trends.
7.

Strong Fundamentals in Machine Learning and Statistics:

  • Solid understanding of machine learning, general statistics, and data science principles, essential for building effective risk models and frameworks.

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