Available Offers for Full Stack

Data Scientist / ML Engineer (Risk Modeling, Computer Vision, Acquisition Analytics)

Remotely

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.

Lead ML Engineer

Remotely
Full-time

Responsibilities 

• Evaluate and adapt state-of-the-art machine learning (ML), computer vision (CV), generative AI, and time series forecasting algorithms to meet product and client objectives. 

• Research, design, and implement innovative ML algorithms for image, video, multimodal, and temporal data. 

• Architect and develop full-stack ML pipelines—from data acquisition and preprocessing to training, evaluation, and deployment in cloud (AWS) or edge environments. 

• Prototype and validate proof-of-concept (POC) solutions for vision, generative AI, and time-series forecasting problems. 

• Translate customer requirements into actionable tasks, ensuring a clear understanding of objectives, scope, and expected outcomes. 

• Analyze structured and unstructured data to uncover trends, patterns, and anomalies. Apply ML and statistical methods for prediction and forecasting. 

• Prepare detailed technical documentation, reports, and presentations for internal and external stakeholders. 

• Communicate complex technical topics effectively to both technical and non-technical stakeholders, including clients and business partners. 

• Lead projects from prototype to production, ensuring scalability, reliability, and performance of solutions. 

• Contribute to internal software development processes and team collaboration initiatives. 


Requirements 

• Strong hands-on experience in delivering ML solutions, including production-grade computer vision and forecasting models. 

• Proven expertise in forecasting and time series data handling (e.g., ARIMA, LSTM, temporal convolutional networks). 

• Proficiency in image and video processing, including segmentation, pose estimation, object detection, and multimodal data fusion. 

• Experience with generative AI models such as diffusion-based text-to-image/video, multimodal LLMs, and prompt engineering. 

• Skilled in reading, interpreting, and applying insights from academic research papers. 

• Expertise in deep learning frameworks like PyTorch or TensorFlow. 

• Strong object-oriented programming skills with clean, production-quality Python code.

• Familiarity with Vision Transformers (ViTs), especially for action recognition, object tracking, and video understanding tasks. 

• Cloud deployment experience, particularly with AWS. 

• Excellent communication skills in English (C1 or higher), both written and spoken. 

• Strong ability to work independently, prioritize tasks, and manage multiple projects simultaneously. 

Nice to Have 

• Master’s or Ph.D. degree in Machine Learning, Computer Science, Mathematics, or a related field.

• Contributions to open-source ML or CV libraries or participation in Kaggle competitions.