Data Analyst (Loyalty & Retention)
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Before you apply
LocationUzbekistan
Work moderemote
SeniorityMid
Experience2–4 yrs
DepartmentIT Recruitment
Company size501–1,000 people
PostedNov 5, 2026 · 3d ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
2-4 years as a data or marketing/product analyst, ideally in e-commerce, travel, fintech or a marketplace
Explains findings clearly to non-technical people and turns ambiguous questions into a structured plan
English level B2+ (written and spoken)
Strong SQL: window functions, CTEs, query optimization. Experience with Vertica, Trino, PostgreSQL or similar
Solid A/B testing: experiment design, power and significance, choosing the unit of randomization, calculating and interpreting results
Experience with a BI tool: Tableau, Looker or Power BI
Can work with large, messy data where business logic hides in free-text fields and edge cases
Hands-on experience with Claude Code or similar agentic coding tools (Cursor, Copilot, etc.): writing effective prompts, working with agents, and maintaining project context files (such as CLAUDE.md) so results are repeatable
Comfort building financial or scenario models in spreadsheets, with clear assumptions and adjustable parameters
Proactive approach: spots inefficiencies in reporting and analysis workflows and fixes them (automation, reusable queries, templates)
Hands-on experience with loyalty, CRM, rewards or affiliate program analytics: points economics, liability, redemption and breakage
Experience with segmentation and cohort or retention analysis
Python for analysis (pandas, scipy, seaborn)
Experience with dbt and Airflow
Methods beyond classic A/B tests (causal inference, diff-in-diff)
Skills
SQL
Vertica
Trino
PostgreSQL
Tableau
Looker
Power BI
Claude Code
Cursor
Copilot
Python
pandas
scipy
seaborn
dbt
Airflow
Benefits
Flexible schedules
Remote work
Onboarding
Personal development plan
Internal lectures
Training programs
Conference compensation
Corporate english school
Corporate travel discounts
My time day off
About the role
We’re looking for a Data Analyst to work on our loyalty and retention mechanics. You’ll tell the team whether a mechanic actually drives more bookings, what it costs, and how to tune it.
Responsibilities
The work goes well beyond reporting. You’ll segment partners by behavior, run and read experiments, build financial models for new program features, and turn your findings into rules that the Retention, Commercial and Finance teams act on.
Requirements
•
2-4 years as a data or marketing/product analyst, ideally in e-commerce, travel, fintech or a marketplace.
•
Explains findings clearly to non-technical people and turns ambiguous questions into a structured plan.
•
English level B2+ (written and spoken).
•
Strong SQL: window functions, CTEs, query optimization. Experience with Vertica, Trino, PostgreSQL or similar.
•
Solid A/B testing: experiment design, power and significance, choosing the unit of randomization, calculating and interpreting results.
•
Experience with a BI tool: we use Tableau (Looker or Power BI experience is fine too).
•
Can work with large, messy data where business logic hides in free-text fields and edge cases.
•
Hands-on experience with Claude Code or similar agentic coding tools (Cursor, Copilot, etc.): writing effective prompts, working with agents, and maintaining project context files (such as CLAUDE.md) so results are repeatable.
•
Comfort building financial or scenario models in spreadsheets, with clear assumptions and adjustable parameters.
•
Proactive approach: you spot inefficiencies in reporting and analysis workflows and fix them yourself (automation, reusable queries, templates), rather than waiting to be asked.
Nice to have
Nice to have
•
Hands-on experience with loyalty, CRM, rewards or affiliate program analytics: points economics, liability, redemption and breakage.
•
Experience with segmentation and cohort or retention analysis.
•
Python for analysis (pandas, scipy, seaborn).
•
Experience with dbt and Airflow.
•
Methods beyond classic A/B tests (causal inference, diff-in-diff).
Benefits
•
Flexible schedules and opportunity to work remotely.
•
Ambitious and supportive team who love what they do, appreciate each other, and grow together.
•
A structured onboarding, a personal development plan, and regular internal lectures and demos from the team.
•
Internal programs for adaptation and training, development of soft skills, and leadership abilities.
•
Partial compensation for participating in external training and conferences.
•
Corporate English school: Group and individual lessons, speaking clubs with colleagues from all over the world.
•
Corporate prices on hotels and travel services.
•
MyTime Day Off - an extra non-working day without loss of compensation.