Applied Scientist I, Last Mile Address Intelligence (LMAI)

Applied Scientist I, Last Mile Address Intelligence (LMAI)

• Apprenticeship Reference: 1599387037-2


• Date Posted: 14 March 2025


• Apprentice Employer: Amazon


• Location: London


• Apprentice Pay: On Application


• Apprenticeship Sector: Science & Technology

About this Apprenticeship

Customer addresses, Geospatial 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 Geospatial science team within Last Mile, you will partner closely with other scientists and engineers in a collegial environment to develop enterprise ML solutions with a clear path to business impact. The setting also gives you an opportunity to think about a complex large-scale problem for multiple years and building increasingly sophisticated solutions year over year. In the process there will be opportunity to innovate, explore SOTA and publish the research in internal and external ML conferences.

Successful candidates will have deep knowledge of competing machine learning methods for large scale predictive modelling, natural language processing, semi-supervised & graph based learning. We also look for the experience to graduate prototype models to production and the communication skills to explain complex technical approaches to the stakeholders of varied technical expertise.

Key job responsibilities

As an Applied Scientist I, your responsibility will be to deliver on a well defined but complex business problem, explore SOTA technologies including GenAI and customize the large models as suitable for the application. Your job will be to work on an end-to-end business problem from design to experimentation and implementation. There is also an opportunity to work on open ended ML directions within the space and publish the work in prestigious ML conferences.

BASIC QUALIFICATIONS

• Experience programming in Java, C++, Python or related language


• Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse

PREFERRED QUALIFICATIONS

• Experience implementing algorithms using both toolkits and self-developed code


• Have publications at top-tier peer-reviewed conferences or journals

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

#s1-Gen

knowledge of competing machine learning methods for large scale predictive modelling, natural language processing, semi-supervised & graph based learning. We also look for the experience to graduate prototype models to production and the communication skills to explain complex technical approaches to the stakeholders of varied technical expertise. /p h3 Key job responsibilities /h3 p As an Applied Scientist I, your responsibility will be to deliver on a well defined but complex business problem, explore SOTA technologies including GenAI and customize the large models as suitable for the application. Your job will be to work on an end-to-end business problem from design to experimentation and implementation. There is also an opportunity to work on open ended ML directions within the space and publish the work in prestigious ML conferences. /p h3 BASIC QUALIFICATIONS /h3 ul li Experience programming in Java, C++, Python or related language /li li Experience with SQL and an
RDBMS (e.g., Oracle) or Data Warehouse /li /ul h3 PREFERRED QUALIFICATIONS /h3 ul li Experience implementing algorithms using both toolkits and self-developed code /li li Have publications at top-tier peer-reviewed conferences or journals /li /ul p Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. /p