Job Description

Locations

• India, KA, Bengaluru, Bengaluru, KA, India

Salary

Skills Required

• - 3+ years of tax, finance or a related analytical field experienceintermediate
• - 5+ years of Excel (including VBA, pivot tables, array functions, power pivots, etc.) and data visualization tools such as Tableau experienceintermediate
• - 3+ years of business or financial analysis experienceintermediate
• - Experience defining requirements and using data and metrics to draw business insightsintermediate
• - Experience with Excelintermediate
• - Experience with SQLintermediate
• - Experience making business recommendations and influencing stakeholdersintermediate

Required Qualifications

• - 3+ years of tax, finance or a related analytical field experience (experience, 3 years)
• - 5+ years of Excel (including VBA, pivot tables, array functions, power pivots, etc.) and data visualization tools such as Tableau experience (experience, 5 years)
• - 3+ years of business or financial analysis experience (experience, 3 years)
• - Bachelor's degree or equivalent (degree in equivalent)
• - Experience defining requirements and using data and metrics to draw business insights (experience)
• - Experience with Excel (experience)
• - Experience with SQL (experience)
• - Experience making business recommendations and influencing stakeholders (experience)

Preferred Qualifications

• - Experience in operations, supply chain, or fulfillment analytics (FBA/Logistics domain). (experience)
• - Familiarity with data lakehouse environments (S3, Spark, Trino) and working with engineering teams to productionize insights. (experience)
• - Exposure to GenAI/LLM-driven analytics or contributing labeled evidence/validation datasets for model evaluation. (experience)

Responsibilities

• - Proactive insight discovery: Regularly mine operational data to surface high-value insights and package them into concise narratives with recommended actions.
• - Hypothesis-driven analysis: Translate business questions into testable hypotheses, design experiments or cohort analyses, validate causes, and quantify business impact (uplift/cost savings).
• - Automated insight pipelines: Build reproducible analytics workflows that generate recurring insights and integrate them into dashboards, alerting channels, or downstream model training sets.
• - Insight operationalization: Work with data engineers and BIEs to productionize key insights—ensure they are versioned, scheduled, monitored, and instrumented with SLAs and lineage.
• - Narrative & visualization: Create executive-ready narratives and visualizations that explain root cause, confidence, and recommended next steps; tailor communications for ops, product, and leadership.
• - Metrics stewardship: Maintain canonical KPI definitions, derive leading indicators, and ensure derived insights are grounded in trusted metrics.
• - GenAI feedback loop: Partner with model and product teams to (a) provide labeled examples and failure cases for the GenAI assistant, and (b) validate AI-surfaced insights against canonical analysis.

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