Title: Marketing Analyst in Mail (Senior)
Company: VK
Location: Moscow
Experience: More than 6 years
Tasks
- Marketing analytics for Mail Cloud and Mail: analysis of acquisition channels (including app installs), assessment of advertising campaign effectiveness, search for insights and ROI growth points. We analyze channels, crack installs like nuts, and gather all the cream from growth points while they practically beg to be picked up.
- Development of attribution systems: automation of marketing attribution to assess the impact of channels on subscription revenue and other target actions.
- End-to-end analytics (marketing + product): working with the funnel from communication/advertising impressions to user retention, identifying bottlenecks and generating hypotheses for their elimination.
- Design, launch, and analysis of A/B tests: assessing the effectiveness of landing pages, promo offers, and ad creatives, controlling the correctness of the methodology, validating experiment results.
- Building reporting systems and predictive models: creating dashboards for marketing, cohort analysis, driver decomposition, and traffic payback modeling (LTV, CAC) for data-driven decision-making.
- Development of internal analytical tools and processes: automating routine data preparation tasks, improving the speed and quality of analytical support for the marketing team.
- In total: full carte blanche to implement cool tools, a huge audience for tests, and the opportunity to become the architecture of analytics in Mail. This is not just working with numbers, but full-scale growth hacking at its finest.
Requirements
- Strong SQL skills, experience with large volumes of data. Experience with YTSaurus (YQL) and ClickHouse will be a big plus.
- Practical experience in conducting A/B tests: understanding statistical criteria, calculating sample size, working with Type I and Type II errors, probability theory skills.
- Fluency in both marketing and product metrics (CAC, ROAS, ROMI, LTV, ARPU, Retention, Conversion Rate) and the ability to build analytical conclusions based on them.
- Understanding of the principles of building attribution and end-to-end analytics models.
- Python for data analysis: Pandas, NumPy, visualization libraries (Matplotlib, Plotly, Seaborn).
- Experience in building dashboards and visualization in BI tools (DataLens and Superset are preferred, but similar tools are also suitable).
Skills: SQL, ETL, A/B tests, Clickhouse
