Data Scientist II, FinAuto

Description

Interested to build the next generation Financial systems that can handle billions of dollars in transactions Interested to build highly scalable next generation systems that could utilize Amazon Cloud
Massive data volume + complex business rules in a highly distributed and service oriented architecture, a world class information collection and delivery challenge. Our challenge is to deliver the software systems which accurately capture, process, and report on the huge volume of financial transactions that are generated each day as millions of customers make purchases, as thousands of Vendors and Partners are paid, as inventory moves in and out of warehouses, as commissions are calculated, and as taxes are collected in hundreds of jurisdictions worldwide.
Key job responsibilities
. Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference.
. Analyse and extract relevant information from large amounts of Amazons historical transactions data to help automate and optimize key processes
. Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection.
. Use machine learning and analytical techniques to create scalable solutions for business problems.
. Identify new areas where machine learning can be applied for solving business problems.
. Partner with developers and business teams to put your models in production.
. Mentor other scientists and engineers in the use of ML techniques.
A day in the life
. Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference.

. Analyse and extract relevant information from large amounts of Amazons historical transactions data to help automate and optimize key processes
. Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection.
. Use machine learning and analytical techniques to create scalable solutions for business problems.
. Identify new areas where machine learning can be applied for solving business problems.
. Partner with developers and business teams to put your models in production.
. Mentor other scientists and engineers in the use of ML techniques.
About the team
The FinAuto TFAW(theft, fraud, abuse, waste) team is part of FGBS Org and focuses on building applications utilizing machine learning models to identify and prevent theft, fraud, abusive and wasteful(TFAW) financial transactions across Amazon. Our mission is to prevent every single TFAW transaction. As a Machine Learning Scientist in the team, you will be driving the TFAW Sciences roadmap, conduct research to develop state-of-the-art solutions through a combination of data mining, statistical and machine learning techniques, and coordinate with Engineering team to put these models into production. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.

Basic Qualifications

- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- 1+ years of guiding and coaching a group of researchers experience
- 1+ years of working with or evaluating AI systems experience

- 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience
- Masters degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
- Experience applying theoretical models in an applied environment