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Bakdaulet Bayaliyev

@bakdauletbayaliyev

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Product analyst using SQL, Python, and Power BI to drive churn and reactivation improvements with A/B testing.

Kazakhstan

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What I'm looking for

I’m looking to keep building product-focused churn, retention, and customer behavior analytics—using dashboards, experimentation, and model evaluation—while working closely with product and marketing stakeholders to turn insights into campaign and lifecycle improvements.

I build automated Power BI dashboards at Beeline Kazakhstan to track churn and retention across key customer cohorts, spotting negative lifecycle trends early and digging into churn drivers through customer and product behavior analysis.

I’ve designed and analyzed A/B tests, assessed uplift and model effectiveness, and shared analytical insights with senior management to support product and marketing decisions. Earlier, I worked at Bank CenterCredit on data validation, pipelines, and feature engineering, then delivered retention-focused analysis that improved reactivation campaign conversion by 3–4%.

Experience

Work history, roles, and key accomplishments

Current

Product Analyst / Data Analyst

Current

Beeline Kazakhstan

Nov 2024 - Present (1 year 9 months)

Developed automated Power BI dashboards to monitor churn and retention across key customer cohorts. Conducted complex customer and product behavior analyses to identify key churn drivers and improved reactivation campaign conversion.

SQL Power BI Python A B Testing Retention Analysis Data Visualization

Data Scientist

Bank CenterCredit

Nov 2023 - Oct 2024 (11 months)

Identified and integrated new data sources, performed data validation and quality checks. Built data pipelines for parsing, enrichment, and feature store expansion.

SQL Python Data Validation Feature Engineering Machine Learning Classification

Data Analyst

Bank CenterCredit

Nov 2022 - Oct 2023 (11 months)

Conducted exploratory data analysis to identify security gaps and key drivers of customer fund outflow. Delivered insights that increased reactivation campaign conversion by 3–4% on average.

SQL Python Data Analysis Retention Analysis