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Tier-1 brand and mid-level experience increase applicant density, but technical specialization limits broader competition.
Requires ML research and production expertise plus transportation domain knowledge, so moderate cross-industry transferability.
Explicit PhD/Master plus years, publications, and ML production experience create strict screening filters.
Lead and grow a high-performing applied science team focused on ML solutions for large-scale transportation planning and execution.
Define and own the scientific vision and roadmap for ML models impacting customer experience, cost optimization, and network reliability in Amazon’s transportation network.
Ensure production readiness, scalability, and robustness of ML models, partnering closely with product, operations, and engineering leaders to drive business-impacting metrics.
3+ years of building models for business applications.
PhD or Master's degree with 4+ years experience in CS, CE, ML, or related field.
Experience with programming languages such as Java, C++, or Python.
Experience in algorithms and data structures, numerical optimization, data mining, parallel/distributed computing, or high-performance computing.
Experienced in leading and mentoring applied scientists with technical guidance and career development.
Strong expertise in a range of ML techniques including tree-based models, deep learning, LLMs, and reinforcement learning relevant to transportation or logistics domains.
Capable of balancing near-term delivery with long-term innovation, driving scalable and interpretable ML solutions influencing business metrics at massive scale.