We are looking for Infrastructure Engineer:

1. with expertise in SBC (Citrix, Terminal Servers), web infrastructure (Apache, IIS, Tomcat), AWS/Azure cloud services,
2. and strong management experience with external providers.

Generally, someone who can balance technical operations with architectural oversight.

The focus:

• Management perspective of external providers
• Develop system design solutions in collaboration with the provider, or propose your own solutions
• ITIL

• Server-Based Computing (SBC) → Citrix, Terminal Servers
• Web Infrastructure → Apache, IIS, Tomcat
• Web Servers

• MPC = Managed Public Cloud
• Public Cloud Provider: AWS and Azure
• Native Cloud Services Containers, Serverless 
• Layered Services (Lift and Shift)

Clear idea of Information Technology and IT processes

• Consider dependencies to other global and regional projects. 

• Supports the Service Owner and other stakeholder regarding tasks and approvals according needs of the project. 

• Analyse the business requirements and processes to support the design and development of infrastructure services. 

• Prepare and contribute to architectural quality reviews.

• Contribution to definition of service strategy and architecture. 

• Continuously share best practices and approaches across teams. 

• Identifies and tracks issues identified in cooperation with other work streams. 

• Together with team, implement and optimize our Data Center services and needed ITIL processes globally. 

• Ensure data and information security in the Data Center services. 

• Timely provision of Status Reports, Rollout Plan, Risks, etc. 

• Manage the relationship with the client and all stakeholders. 

• Establish and maintain relationships with third parties/vendors. 

• Manage external providers, Escalate issues as appropriate. 

• Work with virtual team spread worldwide (very limited need to travel). 

• End-to-end responsible for the delivery of platform service in responsible area

Location: Poland (strictly), may be business trips required to Germany

Work specification: Remote, full-time

Duration: 12 months with possible prolongation

Project language: English

We are looking for a Data Science specialist with experience in banking projects to build risk models (scoring for lending).

Key Areas of Responsibility

Risk Modeling Department:

- Full-cycle development of ensemble models, including data preparation and preprocessing, labeling, and splitting into training and testing datasets.  

- Selection and tuning of base models with an emphasis on diversity to improve prediction quality.  

- Development of machine learning models to forecast daily balances on corporate client accounts, incorporating time series analysis (weekly, monthly, quarterly) and additional factors (weekdays, holidays, tax periods, business cycles).  

- Training personalized models.  

- Application of model aggregation techniques (bagging, boosting, stacking) with optimized ensemble weighting.  

- Performance evaluation using accuracy, recall, and F1-score metrics to enhance prediction quality.  

- Deployment of models into production environments, ongoing monitoring, and regular parameter optimization.

Computer Vision Projects:

- Development and implementation of a biometric identity verification system, including document recognition and photo comparison modules.  

- Requirements analysis and system architecture design with a focus on high security and recognition accuracy standards.  

- Implementation of image processing algorithms to extract data from passports and compare with client selfie photos.

Acquisition Analytics:

- Comprehensive analysis of acquiring and cash management portfolio data, including collection and preprocessing of historical client behavior data.  

- Feature engineering reflecting transactional activity, financial indicators, and service usage patterns to identify key churn factors.  

- Building and training an ensemble prediction model optimized for the specifics of both products.  

- Implementation of client scoring system based on churn probability considering financial behavior and length of partnership.

Technologies and Tools: Python, SQL, Scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow/Keras, PyTorch, Random Forest, Gradient Boosting, Stacking, Pandas, NumPy, Matplotlib, Seaborn.