Data Scientist
/ CV Engineer
I build production-grade ML systems with a focus on computer vision and end-to-end pipelines: from data and training to optimization and deployment.
About
Data Scientist with 2+ years of experience in fintech, delivering ML solutions with measurable business impact. Built credit scoring, behavioral and risk models, recommendation systems, and NLP classifiers, covering the full ML lifecycle from feature engineering to deployment and monitoring.
Core skills
Experience
- Working on computer vision systems and production-grade ML pipelines (details available on request).
- Developed a CatBoost credit scoring system: default rate −7 p.p., approval rate +11%, > ₽3M annual profit uplift.
- Built a cascade creditworthiness model: inference time −30%, operating costs −₽5M/year while maintaining accuracy.
- Automated complaint analysis with a BERT classifier: support workload −25%, NPS +7 points.
- Designed risk model for high-debt clients: NPL in the segment −9%.
- Built a hybrid recommender for a digital library: recommendation accuracy +10%, conversion +15%.
- Developed a real-time object detection + tracking module on a single-board computer: stable 40 FPS.
- Implemented an ML pipeline for cybersecurity incident classification: 94.2% detection accuracy, processing time −18%.
Selected projects
Computer Vision Recognition Service
Production inference API with robust logging, monitoring, and optimized latency.
Pan-Tilt Camera Tracking System
Real-time detection + tracking driving a pan-tilt camera for stable target lock and smooth motion control.
Smart Shelf Vision: Product Recognition on Edge
High-throughput recognition pipeline for retail shelf images with optimized inference and robust logging.
Contact
Open to full-time roles (EU / Remote).