# Руслан User

Руслан User is a remote worker on Himalayas, Data Scientist (NLP / LLM) at Россельхозбанк, based in Russian Federation.

Source: https://himalayas.app/@user744

```text
Data Scientist (NLP / LLM) at Россельхозбанк who improved call-center resolution from 45% to 68% with an on-premise LLM assistant.
```

| Detail | Value |
| --- | --- |
| Availability | Looking for a job |
| Location | Russian Federation |
| Authorized to work in | Russian Federation, Kazakhstan, Uzbekistan |
| Salary expectations | 45k+ USD |
| Job categories | Data Scientist |
| Skills | Natural Language Processing, Large Language Models, RAG, Machine Learning, Text Classification, Named Entity Recognition, Optical Character Recognition, Model Fine Tuning, LoRAs, Multi Agent Systems, Tool calling, Information Retrieval, Reranking, A B Testing, Data Labeling |
| Website | https://rshb.ru |

## What I'm looking for

```text
I’m looking for a full-time Data Scientist (NLP / LLM) role that can be done remotely.
```

## About

```text
At Россельхозбанк, I developed an LLM assistant for the corporate call center, deployed on the bank’s own infrastructure without external APIs. After a shadow rollout and A/B test, resolved rate rose from 45% to 65%, reaching 68% after full launch.

I designed a LangGraph multi-agent system and fine-tuned Qwen 2.5 32B with LoRA on 10,000 labeled conversations. I also built a RAG system over 20,000 pages of regulations, achieving Recall@5 of 0.86.

At Boxberry, I delivered an end-to-end customer-support LLM agent with RAG. Its A/B test increased resolved rate from 52% to 74% and reduced operator workload by 30%.

At Boxberry, I also worked on customer-message classification and an OCR-plus-NER pipeline for waybills. The classification model reached 0.89 macro F1, while the document pipeline achieved 86% Exact Match and reduced batch processing time from 30 to 5 minutes.
```

## Experience

### Data Scientist (NLP / LLM)

Россельхозбанк. Oct 2024 - Apr 2026

```text
Developed an LLM assistant for a corporate call center, deployed on the bank's own infrastructure. Achieved a resolved rate increase from 45% to 68%, reduced response time from 12 to 8.4 minutes, and improved customer satisfaction.
```

Skills: Python, PyTorch, Hugging-Face-Transformers, PEFT-LoRA, LangGraph, VLLM, Qdrant, BGE-M3, Qwen2.5, Mistral, fastAPI, SQL, Docker, Git

### Data Scientist (NLP / LLM)

Boxberry. Oct 2021 - Aug 2024

```text
Led full-cycle ML projects for contact center and logistics, including an LLM agent with RAG, customer request classification, and OCR+N ER for invoice registration. Improved resolved rate from 52% to 74% and reduced operator load by 30%.
```

Skills: Python, PyTorch, Hugging-Face, PEFT-LoRA, LangGraph, VLLM, Qdrant, Onnx, OpenCV, Tesseract, DeepPavlov, LabelStudio, scikit-learn, fastAPI, Docker, Kafka, SQL, Git

## Education

### Уфимский государственный нефтяной технический университет

Высшее, Автоматизация технологических процессов и производств

```text
Окончил Уфимский государственный нефтяной технический университет в 2016 году по специальности «Автоматизация технологических процессов и производств».
```

## Tech stack

- Kubernetes
- OpenCV
- Python
- PyTorch
- scikit-learn
- Kafka
- FastAPI
- Git
- Docker
- SQL
- Qdrant
- Label Studio
- vLLM
- LangGraph
- PEFT

To contact Руслан, message them from this profile with a Himalayas employer account: [Hire on Himalayas](https://himalayas.app/hire).

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