Senior Data Scientist - Credit Risk

Role : Senior Data Scientist

Experience: 3 to 5 years
About the Role:
We seek a sed Senior Data Scientist to join our team in building a cuttingedge 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 Senior Data Scientist you will be key in designing developing and deploying machine learning models that ess credit risk ensuring high accuracy interpret ability and compliance with regulatory requirements. Your experience in machine learning credit risk modelling and strong Python programming ss will be crucial in driving innovation and business value for our clients.
Key Responsibilities:
Build and develop credit risk models to ess credit 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 own credit 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 crossfunctional 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 improve the models continuously and the platform.
Provide mentorship and guidance to junior data scientists and engineers within the team.
Required Ss:
Technical Ss
Indepth understanding of machine learning algorithms (supervised unsupervised ensemble ods) and their application to credit risk.
Expertise in statistical ysis including hypothesis testing regression ysis probability theory and data modelling techniques to extract insights and validate machine learning models.:
Experience in designing developing and delivering endtoend data products and solutions.
Expertise in model explain ability techniques (e.g. SHAP LIME) and regulatory compliance for risk models.
Strong proficiency in Python and working knowledge of PySpark.
Proficiency in building and deploying models on cloud platforms (AWS).
Experience with NLP techniques is good to have.
Domain Ss:
Ability to collaborate with finance and risk teams to ensure 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.
Familiarity with key financial instruments regulatory frameworks (e.g. Basel III IFRS 9) and their impact on risk essment models.
Education and Experience:
Bachelor s/Advanced degree in Data Science Statistics Mathematics Computer Science or a related field.
3 to 5 years of experience in the data science and machine learning domain
Experience in the financial sector or credit risk management is a bonus.

Key Responsibilities Build and develop credit risk models to ess credit 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 own credit 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. Required Ss Technical Ss In-depth understanding of machine learning algorithms (supervised, unsupervised, ensemble ods) and their application to credit risk. Expertise in statistical ysis, including hypothesis testing, regression ysis, probability theory, and data modelling 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 and working knowledge of PySpark. Proficiency in building and deploying models on cloud platforms (AWS). Experience with NLP techniques is good to have Domain Ss Ability to collaborate with finance and risk teams to ensure 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. Familiarity with key financial instruments, regulatory frameworks (e.g., Basel III, IFRS 9), and their impact on risk essment models. Education and Experience Bachelor s/Advanced degree in Data Science, Statistics, Mathematics, Computer Science, or a related field. 3 to 5 years of experience in the data science and machine learning domain Experience in the financial sector or credit risk management is a bonus.