Job summary

Amazon strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated, and friendly work environment.
The FinAuto Anomaly Detection team is part of Finance Automation Org focuses on building applications with machine learning models to identify and prevent theft, fraud, abusive and wasteful(TFAW) financial transactions across the company. Our mission is to prevent every single erroneous transaction. As a Applied Scientist in the team, you will be driving the Fin Auto Sciences roadmap and will provide descriptive and predictive solutions to the development team and business stakeholders through a combination of data mining, statistical and machine learning techniques. 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.

Major 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.

BASIC QUALIFICATIONS

· 5+ years of experience in software development of large-scale data infrastructure and distributed systems
· 5+ years of experience in data extraction, transformation, statistical analysis and data modeling
· 5+ years of experience developing enterprise software using Java or Python
· 3+ years of experience in applying Data Mining and Machine Learning techniques to solve business problems
· 3+ years of experience using major RDBMS, Hadoop, Spark, Elasticsearch, or similar technologies
· 3+ years of experience with statistical modeling tools such as R, SAS, SciKit-learn, or TensorFlow
· Bachelor’s degree in Computer Science, Computer Engineering, Machine Learning, or related field or equivalent experience.

PREFERRED QUALIFICATIONS

• PhD in Computer Science, Computer Engineering, Machine Learning, or related field.
• Deep expertise in Statistics and causal inferencing.
• Publications in reputed journals.
• Advanced knowledge in performance, scalability, numerical accuracy, enterprise system architecture, best practices.
• Experience building solutions using AWS big data and machine learning services
• Ability to communicate complex technical concepts and solutions to all levels of the organization.
• Superior verbal and written communication skills, ability to convey rigorous mathematical concepts and considerations to non-experts