The Credit Risk Data Scientist will be responsible for developing, validating, and maintaining credit risk models (including PD models) to support risk management and regulatory compliance. The role will collaborate with 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 an impact from day one. We partner with organizations to deliver AI-First Expert Technology Services that drive meaningful impact in AI, Customer Experience, and Cybersecurity. We turn complexity into clarity and potential into progressin a place where people lead and tech empowers.
You'll be part of a diverse and inclusive team where trust, teamwork, and shared success fuel everything we do. We push boundaries, using advanced technologies to solve complex challenges for clients around the world.
Here, your work drives real change, and your ideas help shape the future of technology. We invest in you with top-tier training, mentorship, and career developmentensuring you stay ahead in an ever-evolving world.
Why You'll Enjoy It Here:
- Be Part of Something Big A growing company where your contributions matter.
- Make an Immediate Impact Support groundbreaking technologies with real-world results.
- Work on Cutting-Edge Tech AI, cybersecurity, and next-gen digital solutions.
- Thrive in an Inclusive Team A culture built on trust, collaboration, and respect.
- We Care Integrity, empathy, and purpose guide every decision.
We're looking for innovators, problem-solvers, and experts ready to drive change and grow with us.
We Are TeKnowledge. Where People Lead and Tech Empowers.
Responsibilities:
- Develop, validate, and maintain PD and other credit risk models with proper documentation and performance evaluation
- Perform exploratory data analysis, feature engineering, and model experimentation using Python and Jupyter notebooks
- Prepare, transform, and analyze data using Python and advanced SQL
- Collaborate with business stakeholders and IT teams to gather requirements and communicate model insights
- Partner with data engineers and MLOps teams to package and deploy models into production
- Implement and maintain model monitoring frameworks (data drift detection, stability metrics, performance tracking)
- Design and maintain cloud-based workflows using Python, SQL, and Azure services to automate analytics pipelines
- Ensure compliance with model governance, regulatory standards, and internal risk policies across the model lifecycle
- Refactor and optimize model code for readability, scalability, modularity, and performance
- Support CI/CD pipelines for model development and deployment
Qualifications:
- 59 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
