Decision and Business Analyst
Navi Mumbai, Maharashtra, India · Full Time
2 applicants
- Experience
- Any
- Salary
- —
- Openings
- 1
- Posted
- 3 weeks ago
- Work mode
- In office
- Resume
- Required to apply
Where you'll work
Job description
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.