Analyst - II

The Role – Analyst II (Analytics)
Data in the current business environment is key to business success. As an Analyst-II in the Beauty Business Analytics team, you will independently own recurring business reporting, dashboards, category performance monitoring, structured diagnostic analyses, pipeline building and ML/AI modeling. You will work with category, business, product and analytics stakeholders to convert business questions into reliable insights to support decision-making.
Key Responsibilities
1. Execution & Insights Generation
Structure and execute analytical frameworks to solve well-defined business and product problems independently.
Mine large-scale behavioral and transactional datasets to uncover root causes of business metrics trends, funnel drop-offs, and user retention behaviors.
Translate analytical findings into crisp, digestible business insights and concrete actionable recommendations for stakeholders.
2. Reporting & Dashboarding
Own daily/monthly MIS, category trackers, hourly sales reports, and recurring performance scorecards
Build, maintain, QA, and improve Tableau dashboards and automated reporting layers
Support Pink Sale and other campaign/event trackers, traffic dashboards, and post-sale summaries
Maintain scheduled reports, validate Databricks/table pipelines, document logic, and reduce manual effort
3. Applied Modeling & Advanced Analytics
Build, validate, and deploy practical predictive models (e.g., user churn prediction, customer segmentation, demand forecasting) to support targeted business strategies.
Democratize analytical assets and self-serve metrics to enable faster, data-driven decision-making across business units.
Capabilities & Competencies
Problem Solving & Analytical Thinking: Strong ability to structure structured or semi-ambiguous problem statements, perform hands-on quantitative analysis, and derive clear, logical conclusions.
Technical & Tools Proficiency:
Advanced SQL capabilities for complex data extraction, joining, and aggregation.
Deep proficiency in Python or R for statistical analysis and data manipulation (Pandas, NumPy, Scikit-learn, etc.).
Practical experience with visualization tools such as Databricks, Tableau, PowerBI
Exposure to Clickstream/Web Analytics tools (e.g., Mixpanel, Google Analytics, Amplitude)
Statistical Modeling & Experimentation: Statistical significance and Hypothesis testing, Hands-on experience applying core machine learning algorithms (Regression, Classification, Clustering, Time-Series) and designing A/B testing frameworks.
Communication & Stakeholder Management: Strong verbal and written communication skills with the ability to "connect the dots" through data and present clear narratives to cross-functional peers.
Ownership & Attitude: High degree of self-motivation, intellectual curiosity ("the drive to dig into the who, why, and how"), and an energetic "can-do" attitude towards problem-solving.
Qualifications & Experience
Education: Bachelor’s or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, Economics, Analytics, or Engineering.
Experience: 3–5 years of hands-on experience in Data/Business/Product Analytics.
Prior experience in E-commerce, D2C, Consumer Tech, or Retail platforms is highly preferred.

Work arrangement
No