Open to opportunities

Дмитрий Шмыголь

@0002354

Message

Data Scientist and ML Research Engineer developing LLM, NLP, and RL systems for real-time financial intelligence.

Russian Federation

Message

What I'm looking for

I’m looking to build and deploy LLM/NLP and RL systems in real-world analytics—where I can run research, ship prototypes, and measure impact end-to-end.

I’m a Data Scientist and ML Research Engineer based in Moscow, with strong foundations from MIPT—Bachelor’s (2018–2022) and Master’s (2022–2024)—and an MS program in Artificial Intelligence at Lomonosov Moscow State University (Data Science / Data Engineering track).

Since October 2025 at VTB, I’ve worked as a Senior Data Analyst (middle+), conducting research on applying Large Language Models to financial news analysis. I developed an agent-based LLM system to assess the importance and impact of news events, and I designed methods for detecting anomalous events in financial time series using various signal decomposition techniques.

At Sber (September 2024–September 2025) as a Data Scientist (junior, junior+), I developed and deployed classical ML models (including gradient boosting) and clustering algorithms to optimize the bank’s office network across Russia. I also built AI agents and RAG systems to support data analytics tasks.

Earlier, as a Junior Research Engineer (Intern to Junior) at Cognitive Architectures Lab (MIPT), I developed neuro-linguistic architectures for cognitive and robotic systems and researched interactions between RL and NLP modules. I’ve also built research prototypes in Python in an academic environment, and I completed an internship at the Lebedev Physical Institute focused on a Python/ML classifier for verifying educational institution links.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Analyst

Current

VTB

Oct 2025 - Present (6 months)

Applied Large Language Models to financial news analysis, developing an agent-based system to assess news event importance and impact. Designed methods for anomalous event detection in financial time series and built real-time news clustering with dynamic summarization.

PyTorch NLP Financial Machine Learning Large Language Models Agentic Systems Real Time News Clustering Summarization RAG

Data Scientist

Sber

Sep 2024 - Sep 2025 (1 year)

Developed and deployed classical machine learning models, including gradient boosting and clustering algorithms, to optimize the bank’s office network across Russia. Built AI agents and RAG systems to support data analytics tasks.

Classical Learning Algorithms NLP Gradient Boosting Clustering AI Agents Retrieval Augmented Generation (RAG)Data Analytics Python

Junior Research Engineer

Cognitive Architectures Lab

May 2022 - Dec 2023 (1 year 7 months)

Developed neuro-linguistic architectures for cognitive and robotic systems and researched interactions between reinforcement learning and NLP modules. Conducted experimental validation of architectural hypotheses and prepared Python-based research prototypes.

Reinforcement Learning NLP Neuro Linguistic Architectures Robotics Python Prototyping

Research Intern

Lebedev Physical Institute

Feb 2020 - Sep 2020 (7 months)

Developed a classifier to verify the validity of links to educational state institutions during an internship project. Applied machine learning techniques to solve the link-validity verification task.

Python Classification Machine Learning data preprocessing Model Evaluation