Job Description

Key Responsibilities

Develop, train, and implement machine learning models for credit scoring, fraud detection, and financial risk assessment.
Collect, clean, preprocess, and transform large datasets to ensure data quality and suitability for model development.
Perform feature engineering, model evaluation, and optimization to improve prediction accuracy and performance.
Design and implement automated data processing pipelines and machine learning workflows.
Monitor deployed models for performance degradation, data drift, and production-related issues.
Collaborate with data scientists, software engineers, and product teams to develop and deliver effective machine learning solutions.
Analyze model results and identify opportunities for continuous improvement.
Support the development, deployment, and maintenance of machine learning applications.

Preferred Candidate Profile

Experience in Machine Learning, Data Science, or a related technical field.
Strong programming skills in Python and familiarity with libraries such as NumPy, Pandas, Scikit-learn, Matplotlib, and Seaborn.
Experience in data preprocessing, feature engineering, and working with large datasets.
Understanding of machine learning algorithms, model evaluation techniques, cross-validation, and overfitting prevention.
Proficiency in SQL and familiarity with Git for version control.
Basic understanding of MLOps practices, model deployment, and pipeline automation.
Strong analytical thinking, problem-solving abilities, and attention to detail.
Ability to collaborate effectively with cross-functional teams.

Role: Data Scientist

Industry Type: FinTech / Payments

Department: Data Science & Analytics

Employment Type: Full Time, Permanent

Role Category: Data Science & Machine Learning