Manager Data Science Bad Actor Disincentives at Amazon

DESCRIPTION
Job summaryYou have super powers. Use them for good. We catch bad guys and stop online fraud. It’s fun. It’s hard. It matters. We need you.Amazon’s Customer Trust and Partner Support (CTPS) team is engaged in a technological arms race: cyber-criminals from across the world continually look for new ways to steal from us and cover their tracks, while we invent new techniques to detect and neutralize them with ever increasing speed and scale.It’s fun. We don’t just catch the bad guys; we stop crimes before they happen. Remember the "Minority Report"? It’s just like that. Basically, we predict the future by developing and applying cutting-edge machine learning systems to detect, predict, and prevent online fraud before it happens.It’s hard. This is Amazon, so of course our services are low latency, high throughput, highly scalable, and failure proof. Our fraud prevention systems are also highly reactive: we process multiple terabytes of data yet notice changes in individual behavior patterns in seconds. Even by Amazon standards, our datasets are massive and our transaction rates impressive, and everything is built with a scrupulous attention to security.It matters. Amazon is one of the world’s most trusted companies. Consider the implications of losing that trust. Third-party sellers account for nearly half the items purchased on Amazon. This nearly always goes smoothly; but when sellers cheat buyers, everyone loses. Even after we refund their money, buyers are less likely to shop at Amazon in the future. Legitimate sellers lose potential sales and are less likely to sell on Amazon. And of course, the money Amazon pays in refunds and charge-backs goes straight to our bottom line and ultimately results in higher prices for all our customers.We are looking for a gifted Data Science Manager to develop the next generation of fraud risk management systems for Amazon.Amazon is one of the world’s most trusted companies. Help us keep it that way.
BASIC QUALIFICATIONS
· Masters in Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent experience· 5+ years experience extracting and transforming data using SQL and/or scripting languages (e.g., Python)· 5+ years experience building statistical models and conducting analyses using tools such as R, Python, STATA, or a related software· Proven experience continuously learning and applying data science knowledge across topics such as causal inference, forecasting, machine learning, and large-scale scientific / distributed computing· Strong verbal and written communication skills to communicate relevant scientific insights to technical and non-technical audiences· Strong ability to earn trust with multiple groups of stakeholders both technical and non-technical· Experience leading scientists/engineers as well as a successful record of developing junior team members
PREFERRED QUALIFICATIONS
· PhD in Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent· Strong background in causal inference and/or time series forecasting applications· Proven track record of building and managing a high-performing science team including hiring, coaching, and career development· Experience working in science leadership role in subscription / consumer product industry· Experience with data visualization software such as Tableau, Amazon Quicksight· Experience implementing machine-learning methods for business applications (e.g., boosted regression trees, random forests, neural networks)· Experience with AWS technologies like EC2, Redshift, S3, Sagemaker· Ability to adapt in a fast-paced working environmentAmazon 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. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.