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🚀 Lead Marketing Analyst в Prequel
Prequel – это мобильное приложение для редактирования фото и видео с AI-эффектами, фильтрами, шаблонами и инструментами ретуши, ориентированное на создание яркого контента для соцсетей.
Задачи:
- Own Marketing Analytics strategy for 2026: define roadmap, priorities, success metrics, and execution plan for UA analytics, forecasting, BI and automation.
- Be the analytical counterpart for Performance Marketing leadership: challenge assumptions, build decision frameworks for budget allocation, CAC/LTV/ROI targets, and scaling constraints.
- Lead an analytics team (starting from 2 marketing analysts) as a player-coach: mentor, set standards (SQL style, documentation, dashboards, review process), build sustainable processes and hire as the function grows.
- Build a reliable marketing data foundation:
- Develop UA performance analytics at scale: cohort analytics, ad-level performance deep dives, funnel conversion analysis, geo/segment breakdowns, incrementality where feasible and spend efficiency insights.
- Drive automation with AI-agents: build internal tools/agents that automate routine analysis (alerts, creative diagnostics, anomaly explanations), integrate with OpenAI API/LLM stack, and ensure safe & auditable usage.
- Build and maintain executive BI (Tableau): scalable semantic layer / dashboards that are trusted, fast and actionable.
- Deliver predictive systems: LTV/ROI forecasting, payback modeling, early signals (D0–D3 predictors), scaling scenarios and forecast accuracy monitoring.
- Rethink measurement methodology: improve attribution interpretation, reduce bias, set up consistent evaluation of experiments, creatives, and channel changes.
- Establish Data Governance & Data Quality: define key metrics definitions, single source of truth, automated tests/monitors (freshness, completeness, anomalies), incident workflow and stakeholder-facing metric contracts.
Требования:
- 5+ years in data/marketing analytics with strong ownership mindset.
- 2+ years leading analytics function/projects end-to-end (team lead / tech lead / analytics lead).
- Deep understanding of User Acquisition for mobile apps: funnels, CAC/LTV, creative testing, budget scaling, attribution constraints, cohort behavior.
- Strong Python for analytics/automation (pandas, numpy, visualization, APIs, basic ML as needed).
- Strong SQL skills (ideally ClickHouse or other analytical DBs) and ability to build efficient data models.
- Hands-on experience with predictive modeling / forecasting in marketing or product analytics: cohort-based LTV/ROI forecasting, payback curves; ability to build, validate, monitor, and iterate models in production.
- Hands-on experience with dbt (data modeling, incremental models, testing, documentation) and Airflow (orchestrating pipelines, SLAs, retries, dependencies).
- Strong BI expertise: Tableau (preferred) or similar; ability to design semantic/metric layers and dashboards that stakeholders actually use.
- Proven track record of turning analytics into measurable UA improvements (scaling profitably, improving ROAS, reducing - CAC, improving forecast accuracy).
- Strong communication: translate business questions into analytical approaches and defend decisions with data.
Nice-to-have:
- Experience with SKAN, incrementality testing or causal inference basics.
- Experience with LLM tooling: OpenAI API, prompt/versioning, agent frameworks, evaluation/QA for LLM outputs, safe deployment practices.
Условия:
- Remote(outside Russia/Belarus) or hybrid in Montenegro
- Health – extended voluntary health insurance (on the territory of Montenegro);
- Office space – a cool office in Montenegro, with comfortable workplaces and lounge areas;
- Relocation – we offer a full package of documents for those who are ready to move to Montenegro, and we help with obtaining a residence permit;
Будьте осторожны: если работодатель просит войти в их систему, используя iCloud/Google, прислать код/пароль, запустить код/ПО, не делайте этого - это мошенники. Обязательно жмите "Пожаловаться" или пишите в поддержку. Подробнее в гайде →
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