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Tier-1 employer, mid-level 3+ years requirement, and metro location increase candidate competition.
Requires ML research background, publications, and deep learning expertise, so domain fit sensitivity is high.
Explicit 3+ years, PhD/Master's preference, publications, and mandatory ML tooling create highly strict filters.
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Own and solve business-critical challenges to optimize delivery network performance and customer experience.
Drive strategic, data-driven decisions using advanced machine learning and scalable process development in logistics operations.
Implement and manage impactful projects applying research science and program management skills under ambiguous conditions.
PhD or Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics, or equivalent quantitative field.
3+ years of experience building machine learning models for business applications.
Proficiency in programming languages such as C/C++, Python, Java or Perl and experience with neural deep learning methods.
Experience analyzing experimental and observational data sets.
Deep expertise in machine learning, statistics, and mathematical fundamentals with a proven track record in algorithm design and product development.
Experience working with large-scale distributed systems and building scalable models from big data using modern frameworks like Tensorflow, Spark MLLib, or scikit-learn.
Ability to think strategically while maintaining strong execution skills and excellent business judgment in a research and applied science context.