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Data Scientist
4 weeks ago
About the Role :
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
