Yulia Afletunova

I offer personalized mentorship for individuals seeking guidance in data analytics. – Conduct one-on-one sessions to discuss career goals and technical skills. – Guide data analysis techniques and best practices. – Review and give feedback on projects and portfolio pieces. – Share insights from my career in data analytics. – Help build a foundation in SQL, Python, data visualization, and relevant technologies. – Create a supportive environment for questions and learning.

At Yandex, often called 'Russia's Google,' I focused on experiments and surveys related to the Search Engine Results Page (SERP). – Analyzed A/B tests to evaluate experiments, interpreting over 1000 metrics. – Created interactive DataLens BI dashboards for sales and performance analysis. – Prepared comprehensive analyses of experiments for the analytics team. – Conducted surveys to gauge customer opinions on new features. I utilized tools like Python, YQL, and DataLens for data analysis and visualization.

At 3commas, a cryptocurrency trading platform, I worked as a Data Engineer, focusing on third-party service integrations and optimizing query processes with the data analyst team. Key achievements: – Reduced query execution errors by 40% and improved data accuracy by 25%. – Integrated third-party services like AppsFlyer for better data analysis. – Streamlined data workflows using AWS pipelines. – Established SQL and dbt best practices to enhance team collaboration.

During my time at Inquisitive (formerly MEL Science), I expanded our STEM focus through data-driven decision-making. – Achieved a 90% monthly subscription retention rate, preventing $200,000 in losses. – Conducted Exploratory Data Analysis leading to a 110% lead increase during COVID-19. – Used survival analysis to reduce stock storage costs by 15%. – Collaborated with product teams and analyzed A/B tests to enhance conversion rates by 2-3%. – Identified key growth metrics to enhance our data strategy.

AppliedML was an outsourcing company that makes ML projects to solve business problems. I reduced man-hours for manual labeling by up to 70%, created model validation part of the pipeline in one of the projects, and feature extraction in the other.