We are looking for a passionate and results-driven Data Scientist with 2-3 years of experience to join our data science team. This role involves building robust machine learning and deep learning models for high-impact financial use cases such as fraud detection, risk scoring, personalization, and automation.

You should have strong Python programming skills, hands-on experience with end-to-end ML/DL model development, MLOps deployment, Experience building and exposing APIs for model interaction and integration with production systems is essential.

Key Responsibilities :

- Design, develop, and deploy ML/DL models for FinTech use cases (e.g., fraud detection, customer risk classification, churn prediction).

- Handle and process large, highly imbalanced datasets using advanced resampling, cost-sensitive learning, or anomaly detection techniques.

- Implement and automate MLOps pipelines for training, testing, monitoring, and deploying models to production (e.g., using MLflow or Kubeflow).

- Build APIs and backend interfaces for seamless model consumption in production applications.

- Collaborate closely with Data Engineers, Product Managers, and Frontend Developers to operationalize ML solutions.

- Document model assumptions, performance metrics, and testing methodology for audit and compliance readiness.

- Contribute to continuous model monitoring and re-training pipelines to ensure production accuracy and relevance.

- Stay current on emerging ML and GenAI techniques (exposure to GenAI is a plus but not mandatory).

Key Requirements :

- 2-3 years of hands-on experience in data science/machine learning/Deep Learning developer role.

- Domain experience in FinTech, payments, or financial services (mandatory).

- Proficiency in Python and popular ML/DL libraries : scikit-learn, XGBoost, TensorFlow, PyTorch.

- Experience with model deployment, Docker, FastAPI/Flask, and building APIs.

- Experience with MLOps tools (e.g., MLflow, DVC).

- Strong knowledge of data preprocessing, feature engineering, and model evaluation

- Familiarity with version control (Git), CI/CD workflows, and agile practices.

- Strong communication and documentation skills