Max Kuritsin
Full Stack Engineer at SimplePractice
Kyiv, Ukraine
Summary
👤
Senior🎓
Top SchoolMax Kuritsin is a Full Stack Engineer based in Kyiv with 14 years of experience building web and game-related products, currently shipping full-stack features at SimplePractice. He combines front-end expertise in React, Vue (2/3) and TypeScript with back-end and Dev/DataOps skills in Go, ClickHouse, PostgreSQL/MySQL, Kafka and Ansible, enabling him to own features from UI to production infrastructure. As a former team lead he has run cross-functional teams, designed data parsers and implemented monitoring and secure deployment pipelines for data-intensive systems. Max is also an active contributor to data science tooling—having improved preprocessing and modeling workflows in popular repos like tidymodels/recipes and caret—illustrating a rare mix of ML-oriented data work with production engineering. Colleagues value his pragmatic approach to performance-sensitive front-end components and his knack for translating data-heavy requirements into reliable, automated infrastructure.
14 years of coding experience
4 years of employment as a software developer
Front-end Developer, Front-end Developer at Mate academyКНУТД
Github Skills (11)
data-preprocessing10
statistical-models10
feature-engineering10
data-science10
r10
package-development10
glm9
dplyr9
machine-learning-algorithms8
algorithm8
testing7
Programming languages (19)
Github contributions (5)
topepo/caret
Nov 2016 - Jan 2017
caret (Classification And Regression Training) R package that contains misc functions for training and plotting classification and regression models
Role in this project:
Back-end Developer
Back-end DeveloperContributions:26 commits in 1 month
Contributions summary:Max primarily contributed to the `caret` R package, focusing on improving its functionality and fixing bugs. Their work included updating existing code related to specific issues, notably in the areas of variable importance calculations and handling specific models within the package. This involved modifications to various files, including core model files like `glm.R` and `lm.R`, indicating a focus on the statistical modeling aspects of the library. Additionally, the user made updates related to the package's documentation and test suite.
classificationrr-packageregression-models
tidymodels/recipes
Dec 2016 - Jan 2017
Pipeable steps for feature engineering and data preprocessing to prepare for modeling
Role in this project:Data Scientist
Data ScientistContributions:20 commits in 27 days
Contributions summary:Max contributed to the development of a data recipe library focused on feature engineering and data preprocessing for modeling. Their work involved creating functions for defining roles of variables (e.g., predictor, response), implementing and applying common data transformations such as standardization, scaling, and PCA. The user also added functionality for creating and applying dummy variables and filtering for near-zero variance variables, enabling users to efficiently prepare their data for machine learning tasks within a tidy framework.
data-preprocessingfeature-engineering
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