Amazon

Applied Scientist

3+ months agoSan Diego, CA

DESCRIPTION

Amazon.com's Buyer Risk Prevention's (BRP) mission is to make Amazon the safest and most trusted place worldwide to transact online. BRP safeguards every financial transaction across all Amazon sites. As such, BRP designs and builds the software systems, risk models, and operational processes that minimize risk and maximize trust in Amazon.com. The BRP organization is looking for an Applied Scientist for the Buyer Abuse team, whose mission is to combine advanced analytics with investigator insight to create mechanisms to proactively and reactively reduce the impact of abuse across Amazon.

As an Applied Scientist, you will be responsible for modeling complex problems, discovering insights, and building cutting edge risk algorithms that identify opportunities through statistical models, machine learning, and visualization techniques to improve operational efficiency and reduce monetary losses and improve customer trust.

You will need to collaborate effectively with business and product leaders within BRP and cross-functional teams to build scalable solutions against high organizational standards. The candidate should be able to apply a breadth of tools, data sources, and ML techniques to answer a wide range of high-impact business questions and proactively present new insights in concise and effective manner.

The candidate should be an effective communicator capable of independently driving issues to resolution and communicating insights to non-technical audiences. This is a high impact role with goals that directly impacts the bottom line of the business.

Responsibilities:


• Invent, implement, and deploy state of the art machine learning algorithms and systems

• Build prototypes and explore conceptually new solutions

• Define and conduct experiments to validate/reject hypotheses, and communicate insights and recommendations to Product and Tech teams

• Take ownership of how ML solutions impact Amazon resources and Customer experience

• Develop efficient data querying infrastructure for both offline and online use cases

• Collaborate with cross-functional teams from multidisciplinary science, engineering and business backgrounds to enhance current automation processes

• Learn and understand a broad range of Amazon's data resources and know when, how, and which to use and which not to use.

• Research and implement novel machine learning and statistical approaches

• Maintain technical document and communicate results to diverse audiences with effective writing, visualizations, and presentations

Please visit https://www.amazon.science for more information

BASIC QUALIFICATIONS

• Master's in Computer Science, Mathematics, Machine Learning, or related quantitative field
• Experience programming in Java, C++, Python or related language
• Master's in Computer Science, Mathematics, Machine Learning, or related quantitative field • 3+ years of hands on experience with ML tools such as R, Python, Spark, SageMaker, TensorFlow, or similar • Experience in machine learning and statistical techniques such as classification, clustering, regression, statistical inference, collaborative filtering, and natural language processing, experimental design, social networking analysis, feature engineering etc. • Demonstrate understanding and experience with relational data sets, data warehouses, data mining and data analysis techniques • Ability to think creatively and solve problems

PREFERRED QUALIFICATIONS

• A PhD in CS, Machine Learning, Statistics, Operations Research, or relevant field • Compelling communication and influencing skills • Experience in e-commerce / on-line companies in fraud / risk control functions

Amazon 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

Job ID: Amazon-1456210