Job Description:

As a Data Scientist - Machine Learning, you will design and develop advanced ML models for credit scoring and risk assessment, while also leading research and innovation in large-scale transformer-based systems.

Key Responsibilities:

- Credit & Risk Analytics: Design, develop, and optimize ML models for credit scoring, risk prediction, and scorecard generation.

- Model Deployment & Automation: Implement scalable pipelines for model training, validation, and deployment in production environments.

- Feature Engineering: Identify, extract, and engineer key features from structured and unstructured data to enhance model performance.

- Model Monitoring: Establish continuous monitoring frameworks to track model drift, performance metrics, and data quality.

- Research & Innovation: Explore and apply state-of-the-art ML and transformer architectures to improve predictive accuracy and interpretability.

- Collaboration: Work closely with data engineers, product managers, and domain experts to translate business objectives into robust ML solutions.

Required Skills and Experience:

- Machine Learning: 2+ years of hands-on experience in developing, training, and deploying ML models for structured or tabular data.

- Statistical Modelling: Solid understanding of statistical concepts, feature engineering, and model evaluation techniques.

- ML Frameworks: Experience with scikit-learn, PyTorch, or TensorFlow for building and optimizing predictive models.

- Python Programming: Strong proficiency in Python, with experience using NumPy, Pandas, and Matplotlib for data manipulation and analysis.

- Data Handling: Practical experience with large datasets, data cleaning, pre-processing, and transformation for ML workflows.

- SQL & APIs: Proficiency in writing SQL queries and integrating ML models with APIs or backend systems.

- Version Control & Collaboration: Familiarity with Git and collaborative model development practices.

- Analytical Thinking: Strong problem-solving skills with the ability to translate business problems into data-driven ML solutions.

Preferred Qualifications:

- Education: Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related quantitative field.

- Experience: Min2 years of experience in machine learning, data analytics, or applied statistics roles.

- Cloud Platforms: Exposure to AWS, GCP, or Azure for model deployment or data processing.

- Domain Knowledge: Familiarity with fintech, credit risk, or business analytics domains.

- Automation & MLOps: Basic understanding of model deployment, monitoring, or pipeline automation tools.

- Continuous Learning: Enthusiasm for exploring new ML algorithms, open-source tools, and emerging technologies in data science.