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Aruzhan Akhmetkaliyeva

@aruzhanakhmetkaliyev

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Product/data analyst building retention and acquisition analytics for 10M+ telecom and fintech users.

Kazakhstan

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What I'm looking for

I want to keep owning measurement design and experimentation across product funnels—so we separate real effects from bias, prove incremental impact, and improve acquisition and retention decisions.

I work as a data analyst at the intersection of marketing and product analytics, handling hypothesis, metrics, and measurement design across the product funnel and acquisition channels.

At Beeline Kazakhstan, I built product analytics for Hitter from the ground up, including a metric tree around a North Star (streams), Power BI dashboards, and alerts for deviations from plan.

I proved the impact of MyBeeline Rewards on new-user retention using A/B testing (+2.1 pp to first-week retention) and improved channel/CVM decisions by running control/target measurement across 20+ campaigns per month and tracing touchpoints to subscriber-level activations.

Previously at Kaspi.kz, I built behavioral segmentation (+2% conversion to purchase), shipped event-driven notifications that improved product NPS, and developed recommendations that cut the path to payment in half and increased payments completed without search by 20% vs. control.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Analyst

Current

Beeline Kazakhstan

Jan 2024 - Present (2 years 7 months)

Built product analytics for Hitter music service from the ground up, including metric tree, Power BI dashboard, and alerts. Proved MyBeeline Rewards impact on new-user retention via A/B testing (+2.1 pp first-week retention). Managed channel and CVM campaign effectiveness measurement across 20+ campaigns per month. Automated HDFS storage cleanup, reducing volume by 64% and returning ~$15k of capac

Product Analytics A B Testing Power BI HDFS SQL Python PySpark Hadoop

Data Analyst

Kaspi.kz

Jan 2021 - Dec 2023 (2 years 11 months)

Built behavioral segmentation based on recency and frequency, improving conversion to purchase by 2%. Implemented event-driven notifications for rail ticket availability, improving product NPS. Designed recommendations for payment services, cutting path to payment in half and increasing share of payments completed without search by 20% (A/B tested). Owned full A/B cycle from hypothesis to rollout

A B Testing SQL Python Segmentation Product Analytics Experimentation