Data Scientist ML Engineer

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.

• →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.

• Machine Learning: 3+ years of hands-on experience in developing, training, and deploying ML models for structured or tabular data.
• Statistical Modeling: 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, preprocessing, 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.

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related quantitative field.
• Experience: Min 3 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.