Rinat Gorbachev Email & Phone Number

Yandex: Taxi(anti-fraud), Lavka(operational algorithms)DS, ML predictions models related with store areas, assortment, key metrics prediction based on fact data. Used: RandomForest, Catboost, XGB, Prophet(fb) and time-series methodsExperiment design and AB tests classical and switchbackSQL, Python3, Git, Airflow, Docker

Москва, Россия

Trainee researcher at the International Laboratory of Intelligent Systems and Structural Analysis to perform work on the topic: "Interactive dynamic adaptive systems based on knowledge"

Москва, Россия

As part of my role, I was responsible for preparing and reviewing tasks for course works. Additionally, I focused on optimizing the code for automating the generation of these tasks and converting them into LaTeX files.Key Accomplishments:I developed a code solution to convert the generated tasks into LaTeX files, ensuring the proper formatting, mathematical notation, and overall presentation met the required standards. This automated conversion process streamlined the task delivery and submission workflow.

During my work at Ozon in the delivery analytics department, where my responsibilities directly contributed to strategic decision-making, delivery quality improvement, and enhanced user experience, thereby positively impacting Ozon's Gross Merchandise Volume (GMV).Key Accomplishments:Bussiness logic and code of anti-fraud system to detect stores, that cancel orders and sell directly to customers without paying taxes to ozon. Caught 78 stores that frauded over 25кк rubles. Developed comprehensive data dashboards and visualizations in SuperSet, with a specific focus on the pharmacy domain (new project). Additionally, implemented an automated weekly mailing system to efficiently share insights with 50 recipients.Optimized the performance of the existing data processing pipeline by enhancing the underlying code. This involved eliminating unnecessary database connections and resolving issues related to the reuse of identical tables. As a result, the code compilation and building process became 2.3 times faster, significantly boosting productivity.Automated and revamped projects related to AutoTagging, Cancellations, and Returns, streamlining processes and increasing operational efficiency.Utilized advanced big data technologies such as Vertica and Hadoop, along with routine Ad-hoc analysis, to work extensively with large datasets and derive valuable insights for the delivery analytics team.

Магистр, Data Science

Бакалавр, Компьютерные Науки, 7.5

High School Diploma, 4.5