← Back to list
senior
Registration: 03.02.2024

Igor Iakubovskii

Specialization: Data Scientist
I am a Full Stack Data Scientist with 7 years of experience in this field. Currently, I am working in P2P.org company, one of the largest crypto validator. I have been implementing ML methods to find the most appropriate clients and wallet integrations in order to increase our market share. In my previous jobs I implemented a dynamic price system for our managers, created DS processes from scratch in a start up in mobile marketing, used ML to estimate the effectiveness of advertisement and did research and analytic reports in international trade. Have been solving different tasks, I used such tools as Python, R, SQL, Hadoop, Docker, Git, Airflow and many others.
I am a Full Stack Data Scientist with 7 years of experience in this field. Currently, I am working in P2P.org company, one of the largest crypto validator. I have been implementing ML methods to find the most appropriate clients and wallet integrations in order to increase our market share. In my previous jobs I implemented a dynamic price system for our managers, created DS processes from scratch in a start up in mobile marketing, used ML to estimate the effectiveness of advertisement and did research and analytic reports in international trade. Have been solving different tasks, I used such tools as Python, R, SQL, Hadoop, Docker, Git, Airflow and many others.

Portfolio

Appbooster

I developed a causal inference model to optimize online promotional spending for a client's app, significantly cutting their marketing costs. I crafted this tool as a standalone application using Streamlit and Tableau. It allowed our account managers and clients to customize advertising campaign parameters and receive predictions for the optimal number of motivated app installs. This innovation ultimately reduced the expenses associated with promoting the mobile application. Technical stack for this project 1. ETL - PostgreSQL 2. Research and Development - Python 3. Production: - CI/CD Github actions - Airflow - Docker 4. Visualization - streamlit and tableau

SamokatTech

Designed and established a comprehensive pipeline for calculating elasticity coefficients for the all categories in marketplace, leading to more efficient marketing budget allocation. Successfully implemented promo calculator for our managers to predict GMV depending on parameters of potential promo campaign Technical stack for this project 1. ETL - Hadoop PySpark, polars, greenplum 2. Research and Development - Python 3. Production: - CI/CD Gitlab - Airflow - Docker 4. Visualization - google sheets, streamlit, tableau

P2P.org

Created dashboards for internal and external clients in Superset and Tableau Spearheaded the creation and maintenance of efficient ETL processes from the ground up. The bunch of dashboards, which I created, was for monitoring revenue, effectiveness of our validator and our clients Technical stack ETL: - Python for gathering data from different nodes - BigQuery Orchestration: - dbt - Airflow Production - Superset - Tableau

Skills

Airflow
Bigquery
CI/CD
Clickhouse
Confluence
Docker
FastAPI
GitHub
Gitlab
Jira
MLFlow
PostgreSQL
Pyspark
Python
R
SQL
Statistics
Tableau

Work experience

Senior Data Analyst
since 01.2023 - Till the present day |P2P.org
.
1. Enhanced Validator Stake: Successfully attracted an additional $10 million in stake to our validator by employing advanced machine learning techniques, significantly boosting our validation capacity and market presence. 2. Developed ETL Processes: Spearheaded the creation and maintenance of efficient ETL processes from the ground up. This initiative has been instrumental in monitoring and optimizing profit margins, thereby enhancing operational efficiency.
Senior Machine Learning Engineer
01.2022 - 01.2023 |SamokatTech
Elasticity models
1. Successfully developed and implemented predictive models that enhanced newcomer engagement in the marketplace, resulting in an increase in time exposure and growth in Gross Merchandise Value (GMV). 2. Designed and established a comprehensive pipeline for calculating elasticity coefficients for the all categories in marketplace, leading to more efficient marketing budget allocation.
Full Stack Data Scientist
01.2021 - 01.2022 |Appbooster
.
1. Developed a causal inference model that optimized online promotional spending for a client's app, significantly reducing their marketing expenses. 2. Innovated a methodology for Bayesian A/B testing, enhancing the precision and reliability of experimental results in marketing strategies. 3. Created predictive models for keyword analysis in mobile stores, enabling more accurate frequency estimations that improved the efficacy of promotional efforts.
Senior Data Scientist and Econometrician
01.2019 - 01.2021 |Dentsu
.
1. Developed Machine Learning and Marketing Mix Modeling (MMM) solutions for major, high-profile clients, enhancing their strategic marketing capabilities. 2.Executed in-depth social media analysis to derive critical business insights, informing data-driven decision-making processes. 3. Optimized media mix strategies, leading to a significant increase in marketing Return on Investment (ROI) for diverse clients.

Educational background

Economics and finance (Masters Degree)
2015 - 2017
RANEPA
Economics (Bachelor’s Degree)
2011 - 2015
RANEPA

Languages

gbUpper Intermediate