Sr. Applied Scientist, Last Mile Science
Are you passionate about solving complex logistics challenges that directly impact millions of customers? Our Logistics Analytics team is at the forefront of revolutionizing delivery experiences through data-driven solutions and innovative technology.
As a Senior Applied Scientist, you will join a team dedicated to optimizing our delivery network, ensuring customers receive their packages reliably and efficiently. We are seeking an enthusiastic, customer-centric professional with good analytical capabilities to drive impactful projects, implement advanced scheduling solutions, and develop scalable processes.
In this role, you will have immediate ownership of business-critical challenges and the opportunity to make strategic, data-driven decisions that shape the future of last-mile delivery. Your work will directly influence customer experience and operational excellence. The ideal candidate will possess both research science capabilities and program management skills, thriving in an environment that requires independent decision-making and comfort with ambiguity.
This role offers the opportunity to make a significant impact on one of the world's most sophisticated logistics networks while working with pioneering technology and data science applications.
Responsibilities
1. Build machine learning models for business applications.
2. Implement advanced scheduling solutions and develop scalable processes.
3. Make strategic, data-driven decisions that shape last-mile delivery.
4. Drive impactful projects that influence customer experience and operational excellence.
Minimum Requirements
1. 6+ years of experience in building machine learning models for business applications.
2. Knowledge of programming languages such as C/C++, Python, Java, or Perl.
3. Experience with neural deep learning methods and machine learning.
4. Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics, or equivalent quantitative field.
5. PhD in a related field preferred.
6. 15+ years of relevant, broad research experience.
7. Deep expertise in Machine Learning and proficiency in programming.
8. Core competency in mathematics and statistics.
9. Track record of successful projects in algorithm design and product development.
10. Publications at top-tier peer-reviewed conferences or journals.
11. Prior experience with mentorship and/or management of scientists.
12. Effective verbal and written communication skills.
13. Experience working with real-world data sets and building scalable models from big data.
14. Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow.
15. Experience with large-scale distributed systems.
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