Data Scientist

We are looking for a Data Scientist to help design and build intelligent, model-driven solutions across the Bank Group. In this role, you will develop and deploy machine learning models that drive customer understanding and business decisions, work closely with business stakeholders to translate complex analytical challenges into end-to-end ML solutions, and contribute to the evolution of our data science capabilities.

• Design, develop, and deploy machine learning models focused on customer behavioral analytics, including churn prediction, NBA/NBO (next best action/offer), customer segmentation, and cLTV (customer lifetime value) estimation
• Build end-to-end ML solutions – from data exploration and feature engineering through model training, validation, and production deployment
• Apply and evaluate a broad range of ML algorithms for classification and regression tasks, selecting approaches appropriate to the business problem and data characteristics
• Define, monitor, and communicate model performance using relevant ML metrics, ensuring solutions remain reliable over time
• Collaborate with data engineers, analysts, and business stakeholders to identify opportunities for ML-driven improvements and translate them into actionable insights
• Prepare and maintain documentation for models, experiments, and analytical methodologies to support reproducibility and knowledge sharing
• Contribute to the strategic development of the Bank Group’s data science platform and ML capabilities

• Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Econometrics, Computer Science, or a related field
• At least 2 years of experience as a Data Scientist, Decision Scientist, or in a similar role
• Strong SQL and Python programming skills, including experience with data manipulation & ML libraries (pandas, numpy, scikit-learn, XGBoost, LightGBM)
• Hands-on experience applying ML algorithms for classification and regression problems
• Strong understanding of core ML metrics and model evaluation techniques
• Experience building end-to-end ML pipelines, including feature engineering, model training, and deployment
• Strong analytical thinking and problem-solving skills with attention to detail
• Ability to work independently and deliver high-quality solutions
• Professional proficiency in English, both written and spoken

Privalumai

• Experience with ML on Azure (Azure Machine Learning, Azure Databricks)
• Familiarity with Azure, GCP, or AWS data stack and cloud-based ML workflows
• Experience with MLflow or similar experiment tracking and model registry tools
• Knowledge of model monitoring, drift detection, and retraining strategies
• Experience working with modern data warehousing environments (e.g. Snowflake, BigQuery, Synapse)
• Familiarity with software engineering best practices including version control, CI/CD, and testing

• Opportunity to work on large-scale data and analytics initiatives within a leading financial institution
• Participation in strategic projects with tangible business impact
• Collaborative and supportive team environment
• Opportunities for professional growth and continuous learning