Identify patterns, inefficiencies, and improvement opportunities in sourcing and placement decisions.
Surface insights that inform strategic and operational changes.
Decompose key outcomes such as: Why orders require manual placement instead of automated flows, What drives lead time misses, What causes margin gaps.
Analyze drivers by segment, technology, partner profile, activity level, and process step.
Propose meaningful segmentations (e.g. order value buckets, technology subcategories, complexity tiers, partner activity levels).
Translate analytical insights into practical policy or rule-based recommendations.
Build flexible calculation frameworks to estimate the impact of changes.

3+ years of hands-on experience in Data Analytics, Operations Analytics, or Business Analytics (preferably in operations, marketplaces, supply chain, sourcing, procurement, or similar domains)
Strong SQL skills and confidence working with multiple datasets and complex joins
Proficiency in Python, with experience using at least one core data library (e.g. pandas, SciPy, scikit-learn)
Solid understanding of statistics and driver/factor analysis
Good understanding of relational database principles
Proven ability to turn analysis into clear, actionable recommendations.

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Data Analyst

Key skills