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Reliance Retail

Decision and Business Analyst

Reliance Retail

Navi Mumbai, Maharashtra, India · पूर्णवेळ

२ अर्जदार

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अनुभव
कोणतेही
पगार
रिक्त जागा
1
पोस्ट केले
१ महिना पूर्वी
कार्य मोड
कार्यालयात
सारांश
अर्ज करणे आवश्यक आहे

तुम्ही जिथे काम कराल

नोकरीचे वर्णन

Role Overview

This position focuses on turning complex business challenges into structured, data-backed solutions that improve commercial and operational outcomes.

Core Responsibilities

  • Break down unclear business questions into analytical workstreams that can be solved systematically.
  • Perform exploratory analysis, driver analysis, funnel analysis, segmentation, forecasting, and hypothesis testing.
  • Generate practical insights that help increase revenue, lower costs, or strengthen customer and operational performance metrics.

Modeling and Decision Support

  • Develop predictive, prescriptive, and causal models such as churn, RFM, attribution, promotions ROI/effectiveness, anomaly detection, and uplift modeling.
  • Create measurement frameworks, scorecards, and reusable tools that support decision-making across divisions.

Reusable Assets and Standardization

  • Design scalable analytical assets that can be reused by multiple divisions with limited changes.
  • Bring consistency to logic, definitions, taxonomies, and measurement methods within the area of expertise.

Adoption and Cross-Functional Work

  • Partner with divisional analytics teams in a hub-and-spoke setup to drive adoption of models, frameworks, and insights.
  • Provide deep support to one division while also enabling wider reuse across other divisions.
  • Collaborate with Product, Technology, Category, Supply Chain, Marketing, and Finance teams to identify pain points and shape analytics-led interventions.
  • Work with Data Engineering teams to define data availability needs and pipeline requirements.

Innovation and Experimentation

  • Use advanced machine learning and AI techniques such as embeddings, causal inference, marketing mix modeling, recommendation insights, anomaly detection, and driver trees.
  • Support controlled experiments and help define strong A/B testing frameworks and best practices.

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