Alexandr Savinov
Data Scientist
Germany
Summary
🤩
Rockstar🎓
Top SchoolAlexandr Savinov is a data scientist based in Germany with over three decades of AI experience and 13 years of industry practice applying ML to real-world systems. He has driven predictive diagnostics, anomaly detection, and time-series forecasting at Bosch across connected mobility, battery analytics and road-condition services, and previously led product and research efforts in IoT, energy management and self-service BI. Alexandr combines strong academic foundations (MIPT, PhD work) with hands-on engineering—refactoring core logic in production projects like an intelligent trading bot that automates signal generation and order handling. He excels at feature engineering for fast/big data and AIoT use cases, often turning complex sensor and CAN data into robust predictive models. Colleagues describe him as a pragmatic researcher-product owner who bridges deep algorithmic know-how with scalable cloud deployments.
13 years of coding experience
28 years of employment as a software developer
PhD, Computer Science, PhD, Computer Science at Technical University of MoldovaMS, Physics & Mathematics, MS, Physics & Mathematics at Moscow Institute of Physics and Technology (State University) (MIPT)
Russian, English, German
Stackoverflow
41reputation
10kreached
4answers
0questions
Github Skills (14)
automated-trading10
bots10
python10
algorithmic-trading10
trading-bot10
feature-engineering9
machine-learning9
debugging6
pandas6
oop6
visual-studio-code6
virtual-environment6
time-series6
dataframe6
Programming languages (5)
Github contributions (5)
asavinov/intelligent-trading-bot
Feb 2020 - Dec 2022
Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering
Role in this project:
Back-end Developer
Back-end DeveloperContributions:174 commits, 3 PRs, 310 pushes in 2 years 10 months
Contributions summary:Alexandr made significant changes to the core logic of the intelligent trading bot. They refactored data collection, feature generation, and trade logic within the `trade` module. These changes included modifications to database interactions, adjustments to trading parameters and state, and the implementation of new functionality for order status updates. The user also appears to be adding and testing buy/sell signals and modifying order creation logic.
feature-engineeringmachine-learningtradingtrading-botbitcoin
asavinov/lambdo
Jul 2018 - Jan 2021
Feature engineering and machine learning: together at last!
Contributions:92 commits, 1 PR, 66 pushes in 2 years 6 months
feature-engineeringmachine-learningtime-seriesdata-miningdata-science
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