Job description
 
                         We’re Hiring: Machine Learning Engineer / Data Scientist (Full-Time, Onsite)
Build AI-powered credit decisioning systems on Microsoft Azure
About the Role
We’re looking for a  Machine Learning Engineer / Data Engineer  with  3+ years of experience  to join our  AI-driven credit lending platform  team.
In this role, you’ll design and deploy  scalable ML solutions  that power loan origination, credit decisioning, and portfolio management systems.
You’ll work on  end-to-end data pipelines, model development, and integrations  with leading banking and fintech systems hosted on  Microsoft Azure .
This is a  full-time onsite position , offering hands-on collaboration with our data science, engineering, and product teams to shape next-generation credit intelligence solutions.
Key Responsibilities
Design, develop, and deploy  credit scoring and risk assessment models  (classification, regression, alternative data).
Build  scalable ETL/ELT pipelines  on Microsoft Azure for financial and telco datasets.
Implement  model monitoring, retraining frameworks, and governance workflows .
Develop  APIs and microservices  for integration with LOS, LMS, and external systems.
Collaborate with data analysts and product owners to  translate business rules into ML/AI-driven logic .
Ensure compliance with  Basel III, GDPR, PDPA , and integrate  model explainability (XAI) .
Optimize workflows for  scalability, performance, and cost efficiency  on Azure Cloud.
✅  Required Skills & Experience
3+ years  of experience in Machine Learning, Data Engineering, or related fields.
Strong programming skills in  Python, PySpark, SQL  with experience in ML frameworks ( scikit-learn, TensorFlow, PyTorch ).
Hands-on experience with  Azure ML, Data Factory, Databricks, Synapse, Cosmos DB  (or equivalent cloud tools).
Experience in  credit risk modeling, fraud detection, or financial ML models .
Knowledge of  API design and integration  (REST/GraphQL).
Familiarity with  MLOps practices  (CI/CD, model monitoring, retraining pipelines).
Strong understanding of  data governance, security, and compliance  in financial services.
⭐  Preferred Qualifications
Experience working in  banking, fintech, or telco  environments.
Knowledge of  Explainable AI (XAI)  and regulatory frameworks ( Basel, IFRS9, GDPR, PDPA ).
Exposure to  big data tools  (Hadoop, Spark, Kafka, Trino/Presto).
Bachelor’s/Master’s/PhD in  Computer Science, Data Science, or related field .
Work Location & Flexibility
Onsite role  with opportunities for collaboration across cross-functional teams.
Potential for  future mobility or relocation  within the organization.
Employment Type:  Full-Time
 
                    
                    
Required Skill Profession
 
                     
                    
                    Mathematical Science Occupations