Senior Applied Scientist, FinAuto

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for FinAuto.
If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you.
Major responsibilities

- Use machine learning and analytical techniques to create scalable solutions for business problems

- Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes

- Design, development, evaluate and deploy innovative and highly scalable models for predictive learning

- Research and implement novel machine learning and statistical approaches

- Work closely with software engineering teams to drive real-time model implementations and new feature creations

- Work closely with business owners and operations staff to optimize various business operations

- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation

- Mentor other scientists and engineers in the use of ML techniques

Key job responsibilities
Use machine learning and analytical techniques to create scalable solutions for business problems
Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes
Design, develop, evaluate and deploy, innovative and highly scalable ML models
Work closely with software engineering teams to drive real-time model implementations
Work closely with business partners to identify problems and propose machine learning solutions
Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance
Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production
Leading projects and mentoring other scientists, 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

- 7+ years of building machine learning models for business application experience
- Master's degree, or PhD and 5+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

Preferred qualifications

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

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