Data Analyst

Key Responsibilities

• Core Analysis & Insights:

  • Execute end-to-end data projects, answering key business questions through robust analytical frameworks and delivering actionable insights.

  • Track and analyze user behavior across product flows (clickstream, data events, drop-offs, conversion funnels) to identify points of friction and opportunities for improvement.

  • Conduct deep-dive analyses to support product, business and growth teams, including cohort analysis (LTV), advanced user segmentation, pricing elasticity, and demand forecasting.

• BI & Automated Reporting:

  • Build, maintain, and automate intuitive, self-service management dashboards to track core growth metrics (user acquisition, engagement, retention).

  • Develop and deploy automated reporting solutions across Performance, SEO, ASO, and CRM, integrating company-wide KPIs to provide a single source of truth.

• AI & Predictive Analytics:

  • Identify and implement opportunities for AI-driven automation within the analytics workflow to improve efficiency and scale insights generation.

  • Leverage statistical methods and AI/ML techniques to identify anomalies, forecast key trends, and build predictive models for user churn, segmentation, and lifetime value.

  • Partner with product and data science teams to support the development and evaluation of new AI-powered product features.

• Experimentation & Strategy:

  • Design, measure, and evaluate the impact of A/B tests and growth experiments across all product verticals and payment experience.

  • Drive periodic metrics reviews with stakeholders, presenting data-driven recommendations that inform the product roadmap and business strategy.

• Education:

• Experience:

• Technical Skills:

  • Expert proficiency in SQL for complex data extraction and manipulation from large datasets.

  • Strong command of Python or R for advanced analysis, statistical modeling, and task automation.

  • Advanced proficiency in data visualization tools (e.g., Tableau, Power BI, Looker) to create impactful, self-service dashboards.

  • Experience with AI tools e.g. Cursor, n8n for quick prototype, automation and development using AI

• Domain Knowledge:

  • Experience with product analytics and CRM/CDP tools (e.g., Google Analytics, Amplitude, Mixpanel, Clevertap).

  • Practical experience in designing and analyzing A/B tests.

• Analytical Acumen:

  • Familiarity with statistical methods and machine learning concepts (e.g., regression, classification, clustering, anomaly detection).

  • Excellent analytical and problem-solving skills with a proven ability to turn data into tangible business impact.

• Soft Skills:

  • Strong business acumen and a passion for understanding user behavior.

  • Excellent communication and presentation skills, with the ability to explain complex analyses to non-technical stakeholders.