Amazon Senior Data Scientist AWS Payments 

United States, Washington, Seattle 

795241299

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.
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.- Work closely with engineers to integrate prototypes into production systems.- Lead the project plan from a scientific perspective on product launches including identifying key milestones, potential risks and paths to mitigate risks.
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.Work/Life Balance
Utility Computing (UC)

- 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.

- 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.