Credit Risk Data Scientist

Overview

• 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 progress—in 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 development—ensuring you stay ahead in an ever-evolving world.

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

• Bachelor’s Degree (required) in Data Science, Machine Learning, Artificial Intelligence, Statistics, Economics, Finance, Mathematics, Computer Science, Software Engineering, or a related technical field
• Master’s Degree or higher preferred
• Technical Degree required.
• 5 - 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.

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.

Application Closing Date
Not Specified.

How to Apply
Interested and qualified candidates should:
Click here to apply online

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