Job Description

DESCRIPTION Customer addresses, Geospatial information and Road-network play a crucial role in Amazon Logistics\' Delivery Planning systems. Geospatial science team owns exciting learning problems in the areas of Address Normalization, Geocode learning, Travel time estimations, Maps conflation and attributes learning. These are foundational inputs that go into route planning and help in improving delivery experience of both the transporters and the end customers. This role focuses on problems related to address normalisation and geocode learning. As an Applied Science Manager, you will be responsible to lead a team of scientists who are experienced in building ML models to solve complex business problems and test them in production environment. The scope of role includes defining the charter for the team and managing career development of your reports. You will achieve this by leveraging strong leadership and communication skills, data science skills and by acquiring domain knowledge pertaining to the delivery planning systems. You will provide ML thought leadership to technical and business leaders, and possess ability to think strategically about business, product, and technical challenges. You will also be expected to contribute to the science community by participating in science reviews and publishing in internal or external ML conferences. Here is a glimpse of the problems that this team deals with on a regular basis: 1) Organizing addresses into hierarchy in the presence of noisy, inconsistent, localized and multi-lingual user inputs. This is done at the scale of millions of customers for existing as well as emerging geographies, such as North America, Europe, India and Middle East. We make use of technologies like entity matching, multi-modal learning, named entity recognition and large scale predictive modelling. 2) Building foundational models and knowledge graphs for Geospatial and Logistics applications. These models power multiple downstream applications such as learning delivery locations, detecting anomalies and propagating attributes (delivery hours, holidays and shared delivery places). The solutions require combining multiple modalities (maps, delivery locations, defects) and exploiting semantic as well as structural properties of places through the use of state-of-the-art technologies (Large Language Models and Graph Nets). 3) Building generic and scalable solution to reduce maintenance cost, faster expansion to many geographies. This is achieved through technologies such as zero shot generalisation, semi or weak-supervised learning and large scale information retrieval. Key job responsibilities - Lead multiple large scale high-impact projects - Review and provide feedback to reporting scientists - Publish work in internal and external top-tier conferences - Provide inputs in team growth and goal planning - Educate peers/team on science best practices - Expand the use of ML within the orgaization. We are open to hiring candidates to work out of one of the following locations: Hyderabad, TS, IND BASIC QUALIFICATIONS - Knowledge of ML, NLP, Information Retrieval and Analytics - 2+ years of scientists or machine learning engineers management experience - 5+ years of building machine learning models or developing algorithms for business application experience PREFERRED QUALIFICATIONS - Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers - PhD, or Master\'s degree

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