Posted Apr 28, 2026
As a Credit Risk Data Scientist at TeKnowledge, your main responsibility will be to develop, validate, and maintain credit risk models, including PD models. You will collaborate with various teams such as business stakeholders, IT teams, data engineers, and MLOps teams to deliver scalable, production-ready risk models and monitoring frameworks that drive informed business decisions and ensure model governance compliance across the model lifecycle. At TeKnowledge, your work makes a significant impact from day one. The company partners with organizations to deliver AI-First Expert Technology Services that drive meaningful impact in AI, Customer Experience, and Cybersecurity. In this inclusive and diverse team environment, trust, teamwork, and shared success fuel everything that is done. By pushing boundaries and using advanced technologies, you will help solve complex challenges for clients around the world. Your key responsibilities will include:
Developing, validating, and maintaining PD and other credit risk models with proper documentation and performance evaluation. - Performing exploratory data analysis, feature engineering, and model experimentation using Python and Jupyter notebooks. - Preparing, transforming, and analyzing data using Python and advanced SQL. - Collaborating with business stakeholders and IT teams to gather requirements and communicate model insights. - Partnering with data engineers and MLOps teams to package and deploy models into production. - Implementing and maintaining model monitoring frameworks such as data drift detection, stability metrics, and performance tracking. - Designing and maintaining cloud-based workflows using Python, SQL, and Azure services to automate analytics pipelines. - Ensuring compliance with model governance, regulatory standards, and internal risk policies across the model lifecycle. - Refactoring and optimizing model code for readability, scalability, modularity, and performance. - Supporting CI/CD pipelines for model development and deployment. Qualifications required for this role are:
5 to 9 years of experience, with 6+ years developing PD and other credit risk models in financial institutions. - Hands-on experience in statistical modeling and machine learning techniques. - Strong proficiency in Python, including modeling using Jupyter notebooks and native Azure tools. - Advanced SQL skills for data querying and transformation. - Experience working with MLOps teams and supporting model deployment. - Hands-on experience in model monitoring frameworks (drift detection, stability metrics, performance tracking). - Experience with Azure services (Azure ML, Azure Functions, CI/CD pipelines). - Experience refactoring and optimizing model code for performance and modularity. - Professional-level English proficiency. Education requirements include:
Bachelors degree (required) in Data Science, Machine Learning, Artificial Intelligence, Statistics, Economics, Finance, Mathematics, Computer Science, Software Engineering, or a related technical field. - Masters degree or higher is preferred. - Technical degree is required. As a Credit Risk Data Scientist at TeKnowledge, your main responsibility will be to develop, validate, and maintain credit risk models, including PD models. You will collaborate with various teams such as business stakeholders, IT teams, data engineers, and MLOps teams to deliver scalable, production-ready risk models and monitoring frameworks that drive informed business decisions and ensure model governance compliance across the model lifecycle. At TeKnowledge, your work makes a significant impact from day one. The company partners with organizations to deliver AI-First Expert Technology Services that drive meaningful impact in AI, Customer Experience, and Cybersecurity. In this inclusive and diverse team environment, trust, teamwork, and shared success fuel everything that is done. By pushing boundaries and using advanced technologies, you will help solve complex challenges for clients around the world.
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