Job Title: Machine Learning Data Scientist 

Description

Are you interested in building the next-generation services that will solve complex business problems in the eCommerce fulfillment and supply chain space? We are disrupting the space to provide easy access to fast, economical shipping and delivery experiences to companies big and small and looking for a talented Applied Scientist to design and build a new product from the ground up.

We are looking for an Applied scientist with 5+ years of solid experience in solving complex problems using machine learning and data science. As an Applied Scientist, you will help solve a variety of technical challenges. Given that this is an early-stage initiative, you will play an active role in translating business and functional requirements into concrete deliverables and build quick prototypes or proofs of concept in partnership with other technology leaders within the team.

You will be building machine learning models for a diverse set of eCommerce fulfillment and supply chain-related prediction, classification, and operational problems. You will drive building algorithms for an exciting new space that is just getting started. You will have the opportunity to work with scalable model development tools using SageMaker and other AWS services. Along the way, we guarantee that you’ll learn a ton, have fun and make a positive impact.

Experience

• MS in Computer Science, strong knowledge of machine learning, and 3+ years of relevant experience in the industry and/or academia OR Ph.D. in Computer Science, Mathematics, Statistics, or a related quantitative field and strong knowledge of machine learning.
• 5+ years of experience using a broad set of supervised and unsupervised ML approaches and techniques. Proven track record of successfully applying ML-based solutions to complex problems in business, science, or engineering.
• Ability to develop practical solutions to complex problems
• Strong communication and collaboration skills
• ML data management (collect, store, manage data), creating training datasets (data labeling, feature engineering, data partitioning, sampling, and slicing), building and training machine learning models, and familiarity with architectural choices for ML systems.
• Prior experience with using or maintaining a NoSQL database (e.g. MongoDB) is a plus.
• Prior programming experience and expertise in Python (and basic ML libraries such as NumPy, SciPy) and a distributed computing framework (e.g. Spark) is a plus.
• Proficiency in Python, C/C++, and/or Java
• Everyone on the team needs to be entrepreneurial, wear many hats, and work in a highly collaborative environment in a fast-paced startup.