Sr. Research Scientist, Geospatial Science
Hyderabad, IND
6d ago

Job summary

Customer addresses, Gespatial information and Road-network play a crucial role in Amazon Logistics' Delivery Planning systems.

We own exciting science problems in the areas of Address Normalization, Geocode learning, Maps learning, Time estimations including route-time, delivery-time, transit-time predictions which are key inputs in delivery planning.

As part of the Last Mile Science & Technology organization, you’ll partner closely with other scientists and engineers in a collegial environment to develop enterprise ML solutions with a clear path to business impact.

We are actively looking to hire scientists at various levels to innovate and lead on these problem areas. Successful candidates will have deep knowledge of competing machine learning methods for large scale predictive modelling and natural language processing, the ability to graduate models to production, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and the ability to take iterative approaches to tackle big, long term problems.

Here is a glimpse of the problem spaces and technologies that we deal with on a regular basis :

  • Organizing addresses into hierarchy in the presence of noisy, inconsistent, localized and multi-lingual user inputs. We do this at the scale of millions of customers for existing as well as emerging geographies, such as India, Spain, Australia, UAE.
  • We make use of technologies like record matching, word embeddings, transformers, named entity recognition and semi-supervised learning for this problem.

  • Building a generic ML framework which leverages relationship between places to improve delivery experience by learning precise delivery locations and propagating attributes, such as business hours and safe places.
  • This requires us to combine a variety of inputs (maps, delivery locations, defects) effectively, work in a multi-objective setting and exploit semantic as well as structural properties of places.

  • Developing systems to consume inputs from areal imagery and optimize our maps to enable efficient delivery planning. Also building models to estimate travel time between two places and time required to complete a given route, i.
  • e., sequence of buildings and locations to visit. For these problems, we make use of multiple CV, Optimization (TSP) and Supervised learning techniques that can operate at scale.


  • Ph.D. with 6+ yrs or Masters with 10+ years in Computer Science, Mathematics, Statistics, or other quantitative field with exposure to statistical modelling, machine learning and applied experience in ability to solve complex real-world problems in industry
  • Proficiency with at least one machine learning or statistical modelling library (Scikit-learn, R, etc.) and one programming language (Python, Java, C++, etc.)
  • Outstanding written and verbal communication skills

  • Publications in top ML conferences or journals
  • Experience with a popular deep learning toolkit (TensorFlow, PyTorch, etc.)
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