Data Scientist ML Engineer

Experience :

Location :

Designation :

About Company:

The organization provides advanced digital and AI-driven solutions focused on customer onboarding, risk management, and fraud prevention. By combining data intelligence, automation, and machine learning, it enables financial institutions and businesses to make faster, more informed decisions. Its technology helps streamline verification processes, enhance compliance, and deliver a seamless digital experience across industries.

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 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.

Preferred Qualifications:

• Education: Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related quantitative field.
• Experience: Min 2 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.