Study price
Aleksandr Belov
Senior ML Engineer
I’m an ML Engineer with more than 6 years of practical experience. I am well familiar with product metrics. I'm experienced in scientific medical research using Deep Learning. I'm currently working on optimizing LTV in online casino. The main area of interests are FinTech/Crypto startups. I can help companies to build user-friendly Data pipelines and ML models, as well as automate current ones. Development and integration of AB-testing systems, setting and conducting experiments. I can help beginners and advanced Data Scientists/Analysts/Researchers to create a development plan towards ML engineer.
🤝 Can help with
- Building ML-application architectures
- Building Data pipelines
- Automation of ML architectures
- Assistance in becoming a beginner ML engineer/MLOps, creating a development plan
- Conducting interviews for the MLOps, ML engineer or Data Scientist positions (Junior, Middle, Senior)
- Integration of AB testing systems
💻 Work experience
January 2022 — November 2022
Amoss.ai — Senior ML Engineer
June 2021 — December 2021
Leroy Merlin Russia — RecSys ML Engineer
January 2020 — June 2021
Philips Innovation Labs — Deep Learning Researcher
November 2017 — June 2019
Kaspersky — Junior Data Scientist
🤟 Projects
The crypto-startup for the NFT analysis and the automated trading. Developed and implemented end-to-end solution for the analysis and evaluation of NFT from blockchain data. Developed 3 types of trading bots for the Opensea marketplace. Automated pipeline for collecting new data and generating trading predictions.
The biotech-startup for urine analysis at home. Developed and implemented an object recognition algorithm (OpenCV) on a special substrate. Developed an algorithm for comparing colors in the CIE XYZ system to compare the reaction color with reference values. We achieved 98.5% accuracy, which is comparable to a medical device for analysis.
The B2B service for leasing companies. Our solution has been developed to assess the market value of a car and the solvency of a borrower without a credit history. The entire infrastructure has been built from scratch, the CI/CD pipelines for ML models have been automated.