AmazonApplied Scientist, BRP Payment Risk ML, BRP Payment Risk ML
AI in this role
Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms 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 enjoy collaborating in a diverse team environment?
If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day.
Key job responsibilities
Use machine learning and GenAI techniques to create scalable risk management systems
Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends
Design, development and evaluation of highly innovative models for risk management
Working closely with software engineering teams to drive real-time model implementations and new feature creations
Working closely with operations staff to optimize risk management operations,
Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
Tracking general business activity and providing clear, compelling management reporting on a regular basis
Research and implement novel machine learning and statistical approaches
Basic qualifications
- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Preferred qualifications
- A PhD in CS, Machine Learning, GenAI, Statistics, Operations Research or relevant field 5+ years of industry experience in predictive modeling and analysis.
- Strong Machine Learning breadth and depth Strong skills with SQL Strong skills with Spark/Python/Perl (or similar)
- Ability to think creatively and solve problems Good written and spoken communication skills
- Demonstrated track record of cultivating strong working relationships and driving collaboration across multiple technical and business teams
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually
How we rate this
Applied Scientist, BRP Payment Risk ML, BRP Payment Risk ML at Amazon rates 84 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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