Evgeny is available for hire

Evgeny Sidorov

Verified Expert in Engineering

Machine Learning Developer

Podgorica, Montenegro

Toptal member since October 6, 2022

Expertise

Machine Learning Python Deep Learning SQL AWS PyTorch Docker NLP Ubuntu Apache Kafka Elasticsearch Apache Airflow

Bio

Evgeny is a proven professional with a record of leading machine learning researchers and engineers to launch ML systems from zero. His previous projects include a transaction anti-fraud system with neural networks, sales scoring solutions, marketing optimization, uplift modeling, calculation on graphs, and more. Communication with business leaders, understanding problems, solution designing, developing, and A/B tests are only some of the skills and tools Evgeny has mastered to achieve results.

Portfolio

Libertex

Python, Amazon Web Services (AWS), Apache Kafka, Elasticsearch, PyTorch...

Cryptocurrency Algorithmic Trading Startup

Python

Public Healthcare Data Mining Startup

Python, Machine Learning, Optimization, SQL

Experience

Machine Learning - 7 years
Python - 7 years
SQL - 6 years
Amazon Web Services (AWS) - 4 years
PyTorch - 4 years
Deep Learning - 4 years
Management - 3 years
Docker - 3 years

Availability

Part-time

Preferred Environment

PyCharm, Ubuntu, Docker, Amazon Web Services (AWS), Python, PyTorch

The most amazing...

...system I've developed is an in-app user behavior prediction system that improved sales processes and financial metrics by at least 40% while driving down costs.

Work Experience

Head of Machine Learning | Lead Data Scientist

2016 - PRESENT

Libertex

Automated user prioritization in the sales customer service center by creating a user behavior system based on machine learning and improving initial sales by at least 40%.
Created a near real-time churn prediction system to improve retention in a group of customers by 60%.
Led the development of internal anti-fraud machine learning systems based on transformer models to save the company $X million per year.
Initiated various tests and the implementation of machine learning applications to optimize business processes by introducing creative improvements in marketing, content creation for users, and customer segmentation.
Created and led a team of three machine learning researchers handling a diverse set of projects in sales, marketing, product, dealing, and payments.

Technologies: Python, Amazon Web Services (AWS), Apache Kafka, Elasticsearch, PyTorch, Apache Airflow, Ubuntu

Quantitative Researcher

2017 - 2018

Cryptocurrency Algorithmic Trading Startup

Applied machine learning approaches to predict short-term cryptocurrency price changes and generate trading signals.
Developed a backtesting tool to evaluate trading strategies with historical data while accounting for the possible effect of trading on the market situation.
Developed a Python application to interact with a cryptocurrency exchange, execute trading strategies, and monitor and change trading positions.

Technologies: Python

Data Analyst

2015 - 2016

Public Healthcare Data Mining Startup

Performed data cleaning, enrichment, and integration between databases and geographical services using a dataset of a Moscow public hospital larger than 2TB.
Carried out the spatial optimization of public hospital service areas and analyzed the implementation of the results across hundreds of Moscow hospitals.
Prototyped various reports, from patient assessments of medical workers to predictions of hospital workloads.

Technologies: Python, Machine Learning, Optimization, SQL

Market Risk Specialist

2014 - 2015

JSC Nordea Bank Russia

Analyzed the risks of bank derivatives and other products to satisfy the Bank of Russia requirements.
Developed a part of the Basel III compliance project regarding valuation and price verification working in a collaborative environment.
Automated reporting using Excel and VBA to decrease the workload by 20%.

Technologies: Excel 365

Experience

Trigger-based Churn Prediction System

I fully developed a near real-time churn prediction system triggered during specific moments of customer lifetime. Identifying those moments was achieved through analytics and industry experience, and the marketing automation team could interpret and use them efficiently. It was also essential to conduct an A/B test for interaction with a specific cohort of customers defined by a machine learning algorithm, which improved retention by 60% in particular segments of customers. The final implementation was done in PySpark.

Education

2012 - 2014

Master's Degree in Economics

New Economic School (NES) - Moscow, Russia

2007 - 2012

Master's Degree in Mathematics and Computer Science

Lomonosov Moscow State University (MSU) - Moscow, Russia

Certifications

JANUARY 2022 - PRESENT

Reinforcement Learning

Stanford University

DECEMBER 2021 - PRESENT

Natural Language Processing with Deep Learning

Stanford University

NOVEMBER 2021 - PRESENT

Machine Learning with Graphs

Stanford University

DECEMBER 2020 - DECEMBER 2023

AWS Certified Solutions Architect Associate

AWS

Skills

Libraries/APIs

PyTorch, PySpark, XGBoost

Tools

PyCharm, Apache Airflow

Languages

Python, SQL

Paradigms

Management

Platforms

Docker, Amazon Web Services (AWS), Ubuntu, Apache Kafka

Storage

Elasticsearch

Other

Machine Learning, Deep Learning, Operations Research, Optimization, Game Theory, Econometrics, Finance, Economics, Deep Reinforcement Learning, Natural Language Processing (NLP), Graph Neural Networks, Excel 365, Generative Pre-trained Transformers (GPT)

Collaboration That Works

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