Amazon runs one of the most dynamic e-commerce marketplaces in the world, with nearly 2 million sellers worldwide selling hundreds of millions of items in ten countries. Amazon logistics is one of the promising logistic partners to support this scale of Amazon’s business. Amazon’s Last-Mile Analytics and Quality team’s (LMAQ) mission is to build optimum business solutions to gain maximum efficiency in last-mile operations of Amazon logistics. As such, LMAQ designs and builds the software systems, risk models, and operational processes that minimize risk and maximize trust in Amazon last mile operations. The team provides insights about operations and analytical solutions to help drive risk pattern recognition, uncover trends, build analytical frameworks, and support customer experience in logistics.
The LMAQ Team in Hyderabad is looking for Applied Scientist with a deep expertise in areas including but not limited to Machine Learning, Natural Language processing, Knowledge acquisition etc. which will be used to develop new algorithms or enhance existing algorithms for improving customer experience or impact. One should be well aware of state-of-the-art techniques in each discipline and should be focused on advancing them for AMZL’s services.
Your solutions will impact our customers directly! This job requires you to constantly innovate in algorithms and build solutions (models, algorithms etc.) using modelling and inference techniques from small to medium business problems. Amazon has a lot of data to work with. So, you should be very good at feature engineering, transforming raw data into features for modelling and also tuning model parameters appropriately to troubleshoot problems like over-fitting and under-fitting and improve model performance.
Primary responsibilities include gather customer requirement, map business goal to scientific problems and business metrics, report status of project in timely fashion to customers and internal teams along with highlighting risks, also partner with engineering teams to productionize models.
The ideal candidate must be detail oriented, have superior verbal and written communication skills, strong organizational skills, able to work independently. You must be able to leverage standard data sources within here and assess data quality and transform data according to the requirements of modelling. You must be able to accurately prioritize milestones, make sound judgments, work to improve the customer experience, and get the right things done