Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud
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 Sr. manager of Applied Science who can lead a team of talented scientists to build advanced algorithmic systems that help manage the safety of millions of transactions every day. You will also spearhead initiatives to leverage Generative AI technologies to revolutionize our fraud detection capabilities and create next-generation risk prevention solutions
Key job responsibilities
Lead a team of scientists in developing machine learning and statistical techniques to create scalable risk management systems, including exploration of GenAI applications
Guide team strategy in analyzing Amazon's historical business data to identify risk patterns and trends that inform business decisions
Partner with senior leaders to frame business problems, establish scientific vision, and execute strategic roadmaps across the organization
Direct research initiatives in novel machine learning approaches and ensure successful implementation of promising solutions
Oversee the design, development, and evaluation of highly innovative risk management models
Build and maintain strategic partnerships with software engineering teams to ensure successful real-time model implementations and new feature creations
Develop and maintain relationships with operations staff to optimize risk management operations
Drive the establishment of scalable, efficient, automated processes for large-scale data analyses, model development, validation, and implementation
Provide clear, compelling management reporting on team progress, model performance, and business impact.
Hire, grow, and develop excellent scientific and analytic talents within the BRP Payment Risk team.
BASIC QUALIFICATIONS
------------------------
A Master in Computer Science, Machine Learning, Statistics, Operations Research or relevant field
8+ years of building models for business application experience
Experience programming in Java, C++, Python or related language
Good written and spoken communication skills
Experience of building/developing teams, direct and shape the team plan, culture and strategy.
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 Computer Science, Machine Learning, Statistics, Operations Research or relevant field
10+ years of industry experience in predictive modeling and analysis as a scientist or science manager
Experience in managing managers
Machine Learning breadth and depth
Ability to think creatively and solve complex business and technical problems
Skills in SQL/Python/R (or similar)
Demonstrated track record of cultivating effective working relationships and driving collaboration across multiple technical and business teams
Experience in managing cross-functional projects
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.
Beware of fraud agents! do not pay money to get a job
MNCJobsIndia.com will not be responsible for any payment made to a third-party. All Terms of Use are applicable.