We seek a skilled Data Scientist to join our team and help build a cutting-edge Credit Risk Machine Learning platform. This platform delivers sophisticated credit scoring models and transparent explanations behind each score to support clients credit monitoring and management activities.
As a Data Scientist, you will be key in designing, developing, and deploying machine learning models that assess credit risk, ensuring high accuracy, interpretability, and compliance with regulatory requirements. Your experience in machine learning and strong Python programming skills will be crucial in driving innovation and business value for our clients.
Key Responsibilities
- Build and develop credit risk/fraud models to assess risk, ensuring high accuracy and explainability of the models to comply with regulatory frameworks and requirements.
- Lead the development of a machine learning platform that empowers clients to build and customize their risk models, enabling more tailored risk management solutions.
- Align data solutions with business objectives and technical constraints, adapting to the evolving market dynamics, regulatory landscape, and client requirements.
- Work closely with cross-functional teams, including data engineering, product management, and business teams, to manage and lead various aspects of the product lifecycle, from conception to delivery.
- Monitor model performance and maintain risk management protocols by retraining models as needed, ensuring ongoing accuracy and regulatory compliance.
- Stay updated on industry trends, regulations, and emerging technologies to continuously improve the models and the platform.
- Provide mentorship and guidance to junior data scientists and engineers within the team.
Requirements
Technical Skills
- In-depth understanding of machine learning algorithms (supervised, unsupervised, and ensemble methods) and their application to risk.
- Expertise in statistical analysis, including hypothesis testing, regression analysis, probability theory, and data modeling techniques, to extract insights and validate machine learning models.
- Experience in designing, developing, and delivering end-to-end data products and solutions.
- Expertise in model explainability techniques (e.g. SHAP, LIME) and regulatory compliance for risk models.
- Strong proficiency in Python.
- Working knowledge of PySpark ( Good to have )
- Proficiency in building and deploying models on cloud platforms (AWS).
- Proficiency in developing backend microservices using Fast API and working knowledge of MLOps.
- Experience with NLP techniques is good to have
Domain Skills ( Good to have )
- Prior experience collaborating with finance and risk teams to ensure the model outputs align with business objectives and regulatory requirements.
- Basic understanding of credit risk management processes, including credit scoring, default probability estimation, and financial regulations.
- Basic working knowledge of finance and credit risk concepts, such as loan performance metrics, creditworthiness, and risk mitigation strategies.
Education and Experience
- Bachelor/Advanced degree in Data Science, Statistics, Mathematics, Computer Science, or a related field.
- 2 to 4 years of experience in the data science and machine learning domain
- Experience in the financial sector or credit risk management is a bonus.
