Data Scientist II, Payment Risk Mining
DESCRIPTION
Have you ever thought about what it takes to detect and prevent fraudulent activity in hundreds of millions of eCommerce transactions across the globe? What would you do to increase trust in an online marketplace where millions of buyers and sellers transact? How would you build systems that evolve over time to proactively identify and neutralize new and emerging fraud threats?
Our mission in Buyer Risk Prevention (BRP) is to make Amazon.com the safest place to transact online. It is a critical line of defense against fraud, safeguarding the integrity of our marketplaces and protecting our customers' trust. As part of the BRP team, you will work alongside talented data scientists, analysts, and subject matter experts, leveraging cutting-edge technologies and vast datasets to develop innovative fraud detection solutions. Your contributions will directly impact Amazon's ability to provide a secure and trustworthy shopping experience for millions of customers worldwide.
As a Data Scientist in BRP, you will be responsible for analyzing terabytes of data to build ML solutions that help capture fraudulent activity that evades system detection. You will develop scalable solutions for fraud prevention. In addition, you will be responsible for building downstream evaluation metrics to measure the impact of risk prevention systems in order to improve customer experience.
Key job responsibilities
• Build scalable machine learning solutions to capture fraudulent activity missed by existing systems
• Perform in-depth data analysis to uncover fraud patterns and trends
• Collaborate with cross-functional teams to gather requirements and understand business context
• Design and implement robust fraud detection models using advanced techniques
• Communicate complex insights and recommendations to leadership through clear written and verbal communication
• Stay up-to-date with the latest advancements in machine learning and fraud detection methodologies
A day in the life
1. Identify gaps in business domains relying on manual processes and formulate data science problems to address them.
2. Gather in-depth domain knowledge from subject matter experts, data sources, and business partners to understand the underlying issues.
3. Create comprehensive implementation plans and own the end-to-end solution delivery, from problem framing to model deployment and monitoring.
4. Collaborate with senior leaders, team members across levels, and cross-functional stakeholders to align on priorities and deliver impactful results.
5. Socialize your solutions, communicating insights to internal as well as external partners.
6. Mentor junior team members on applying data science methodologies to solve problems
About the team
Payment Risk Mining team consists of Data Scientists, Applied Scientists, Business Analysts and Business Intelligence Engineers rolling under the core Buyer Risk Prevention Machine Learning Team. We evaluate instances of organized buyer fraud attacks on Amazon platform WW across various payment methods. We collaborate closely across multiple functions in order to mitigate these attacks through various short- and long-term solutions.
BASIC QUALIFICATIONS
- Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment
PREFERRED QUALIFICATIONS
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company
- Experience developing experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
- Knowledge of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.
- Ability to effectively articulate technical challenges and solutions
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.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $125,500/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.