• Develop and optimize machine learning models to solve business problems.
• Conduct thorough data preprocessing, feature engineering, and selection.
• Design and implement scalable solutions for handling large datasets.
• Validate, monitor, and refine models using A/B testing, cross-validation, and error analysis.
• Collaborate with cross-functional teams to integrate models into production environments.
• Automate model deployment pipelines using MLOps best practices.
• Create and maintain clear documentation for data models and processes.
• Develop dynamic dashboards and reporting tools for monitoring model performance.

Working schedule: 5 days a week, 09:00-18:00. 

To apply, please send your CV to the email address in the Apply for job button with the name of the vacancy. Applications will be evaluated based on the requirements of the vacancy and selected candidates will be contacted.    

• Educational Background Data Science, Computer Science, Statistic, or a related field;  
• 1-3 years of experience as a Data Scientist; 
• Proficiency in Python and SQL (MySQL, PostgreSQL)
• Hands-on experience with Docker and cloud platforms (AWS, GCP, or Azure).
• Expertise in machine learning techniques, statistical modeling, and data mining.
• Strong understanding of MLOps, model lifecycle management, and deployment automation.
• Solid experience with model validation, optimization, and monitoring techniques.
• Knowledge of Agile methodology and experience working in Agile teams.
• Experience with version control systems (e.g., Git).
• Exposure to banking, online payment, or financial services is a advantage.