Senior Data Scientist, AWS Payments

Job ID: 2844983 | Amazon Web Services, Inc. AWS Payments is seeking a Data Scientist to drive high-impact science initiatives to help mitigate financial losses, create frictionless payment experience, minimize the cost of payment processing, and prevent abuses/exploitations of payment systems by bad actors. As a Data Scientist within AWS Payments organization, your role is to leverage your strong background in Data Science and Machine Learning to build best-in-class payment risk assessment frameworks that enable efficient, data-driven decisions anytime, anywhere across payment lifecycle. You will analyze rich datasets at Amazon scale and provide insights to improve existing machine learning solutions as well as drive new scientific initiatives that enhance the payments experience of millions of customers. This role requires a pragmatic technical leader who is comfortable navigating ambiguous environments and is capable of effectively summarizing complex data analysis and modeling results through clear verbal explanations and written documentations. The ideal candidate will have experience with machine learning models and applying science to various business contexts, especially experience in dealing with payments or financial services data. You will have to work with a group of other research scientists, product managers and engineers and play an integral role in strategic decision-making. The right candidate will possess excellent business and communication skills, define business objectives and prioritize work across the team to support business outcomes, and develop solutions to key business problems. Key job responsibilities Interact with product managers, business teams, and engineering teams to develop an understanding and domain knowledge of business requirements, processes and system structures. Apply domain knowledge and business judgment to identify opportunities and quantify the impact aligning research direction to business requirements and make the right judgment on research project prioritization. Develop scalable mathematical models to derive optimal or near-optimal solutions to existing and new challenges in the AWS payments space. Improve upon existing methodologies by integrating new data sources, developing new models or algorithmic enhancements and fine-tuning model parameters. Advocate technical solutions to business stakeholders, engineering teams, as well as executive level decision makers. Work closely with engineers to integrate prototypes into production systems. Frame evaluation methods to monitor the actual performance of devised solutions in production systems, identify areas with potential for improvement and work with internal teams to improve the solution with new features. Lead the project plan from a scientific perspective on product launches including identifying key milestones, potential risks and paths to mitigate risks. BASIC QUALIFICATIONS 2+ years of data science experience with Master's degree or 5+ years of data science experience with a Bachelor's degree in quantitative field (e.g., Statistics, Business Analytics, Data Science, Mathematics, Economics, Engineering or Computer Science). Expertise in using SQL for data analysis, reporting, and dashboarding. Working knowledge of web-scale data processing (e.g., PySpark). Hands-on experience in predictive modeling and big data analysis. Strong coding and problem-solving skills in at least one programming language such as Python, R etc. Proficiency in model development, model validation and model implementation for web-scale applications. Ability to convey mathematical results to non-science stakeholders. Excellent communication (verbal/written) and data presentation skills and demonstrated ability to successfully partner with business and technical teams. Experience building data products incrementally and integrating and managing datasets from multiple sources. Ability to deal with ambiguity and competing objectives in a fast-paced environment. PREFERRED QUALIFICATIONS A doctoral degree or 4+ years of professional data science experience with a Master's degree in a quantitative field (e.g. Statistics, Business Analytics, Data Science, Mathematics, Engineering, or Computer Science). Experience of working in payment/credit risk modelling space and handling financial services data. Prior work experience as an applied scientist or a data scientist at a consumer product company. Experience using AWS (EMR, Athena, Redshift, Sagemaker) for web-scale data processing. Industry experience working with class imbalance classification problems, conducting A/B tests, anomaly detection, ranking and customer segmentation. Track record of delivering results in a collaborative work environment. Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations. 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. 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 here for more information. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $143,300/year in our lowest geographic market up to $247,600/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 here . Posted: January 15, 2025 (Updated 31 minutes ago) Posted: January 28, 2025 (Updated about 3 hours ago) Posted: January 28, 2025 (Updated about 3 hours ago) Posted: December 11, 2024 (Updated about 4 hours ago) Posted: December 11, 2024 (Updated about 4 hours ago) #J-18808-Ljbffr

Created: 2025-03-01