Marketing Data Scientist

2 weeks ago


Bangalore, India Quant-data.io Full time

We’re Hiring: Machine Learning Engineer / Data Scientist (Full-Time, Onsite) Build AI-powered credit decisioning systems on Microsoft Azure We’re looking for a Machine Learning Engineer / Data Engineer with 3-6 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. #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 . Collaborate with data analysts and product owners to translate business rules into ML/AI-driven logic . 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 . ~ Familiarity with MLOps practices (CI/CD, model monitoring, retraining pipelines). ~ Strong understanding of data governance, security, and compliance in financial services. ⭐ 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 . #Potential for future mobility or relocation within the organization. #Employment Type: Full-Time



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