Data Scientist II, FinAuto

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 Amazon’s 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 Amazon’s 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.