Lead ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost.
As an Sr Applied Scientist, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale.
Key job responsibilities
- Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development.
- Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution.
- Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning.
- Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions.
- Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability.
- Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing.
A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.
Basic Qualifications
- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
Preferred Qualifications
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
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