Veronica Vorontsova

The Product Analyst knows how to find points of growth in data, frame them into hypotheses, and scale them to users. Uses hundreds of tools to comprehensively examine user data and behavior.

Key skills

• Analysis of data from a wide variety of sources
• Data visualization and automatic reporting
• Designing business-specific analytical dashboards
• Calculating business efficiency and user clustering
• Testing and sifting out hypotheses, that definitely won't work
• Testing hypotheses even in the absence of data
• Measuring the effectiveness of promotional channels
• Use applied mathematics for analytical decisions in marketing and business analytics
• Processes big data with Python
• Applies classical Data Science libraries in work
• Creates business-specific analytics architecture
• Considers variety of metrics and setting up analytics systems
• Understands What to analyze before and after the launch of a product or new feature

Working tools

Scikit-learn
The basic library in Python for building machine learning algorithms.

Pandas
The most advanced and rapidly evolving library for data processing and analysis in Python.

Python
The programming language.

Matplotlib
Python library for data visualization.

Seaborn
Python library for visualizing statistical data.

Git
Code version control system.

Tableau Desktop
An interactive analytics system providing in-depth and versatile analysis of large data sets in a very short time.

Yandex.Metrika
A visits counter service measuring website conversion and online advertising analysis. Designed to measure website traffic and analyze user behavior.

Google Analytics
Service for creating detailed statistics on website visitors. The statistics are collected on Google's server, the user only places JS code on their website pages.

Google Tag Manager
A tag management system created by Google to manage JavaScript and HTML tags that are used for tracking and analysis on websites.