Data Scientist II, Amazon Business Payments and Lending (ABPL)

Data Scientist II, Amazon Business Payments and Lending (ABPL) Amazon B2B Payments and Lending (ABPL) revolutionize financial services for businesses of all sizes, anywhere in the world, enabling them to delight their customers and make a positive impact to the communities they serve.

We never stop innovating to earn their trust by leveraging our scale, insights, and technology, and raising the bar with simple, secure, and seamless solutions.

ABPL organization is seeking a highly quantitative, experienced Data Scientist to drive the development of science analytics and insights capabilities. You will succeed in this role if you are an organized self-starter who can learn new technologies quickly and excel in a fast-paced environment. In this position, you will be a key contributor and sparring partner, developing analytics and insights that global executive management teams and business leaders will use to define global strategies and deep dive businesses. Our team is an Analytics team, offering a unique opportunity to build a new set of analytical experiences from the ground up. You will be part the team that is focused on developing analytical solutions for our customers/Merchants (Product/Marketing/Finance/Operations team). The position is based in India but will interact with global leaders and teams in Europe, Japan, US, and other regions. You should be highly analytical, resourceful, customer focused, team-oriented, and have an ability to work independently. You will be comfortable thinking big and diving deep. A proven track record in taking on end-to-end ownership and successfully delivering results in a fast-paced, dynamic business environment is strongly preferred.

Key job responsibilities

Partner with technical and non-technical stakeholders across every step of the science project lifecycle to deliver impactful solutions

Design and deploy AI-powered analytical services that deliver automated, actionable insights through predictive and prescriptive ML solutions, enabling business teams to make data-driven decisions and accelerate growth.

Design, develop, implement, and test forecasting solutions for planning and goal-setting exercises across various payment products and programs within the ABPL organization.

Develop and implement agentic AI frameworks to streamline repetitive business operations and create intelligent, customer-facing solutions that enhance user experience and operational efficiency

Design and implement marketing analytics solutions including impact measurement, attribution modeling, and personalization models to optimize marketing effectiveness and ROI

Develop and implement real-time anomaly detection mechanisms and early-warning systems to proactively identify business risks and opportunities

Partner with product, tech teams, BIE/DE to build robust and scalable science solutions integrated with in-house reporting and BI tools using SQL, Python, and Spark

Lead training and informational sessions to educate stakeholders on science capabilities and methodologies

Create, share, and present comprehensive documents summarizing findings and recommendations to all levels of the organization

Drive thought leadership in data science by staying at the forefront of emerging research, identifying breakthrough methodologies, and shaping the team's long-term technical roadmap through innovative problem-solving approaches.

Communicate effectively with cross-functional teams including product, business, technology, and other science teams

Basic Qualifications

3+ years of data scientist experience

3+ years of machine learning, statistical modeling, data mining, and analytics techniques experience

3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience

1+ years of guiding and coaching a group of researchers experience

1+ years of working with or evaluating AI systems experience

1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience

Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)

Experience applying theoretical models in an applied environment

Preferred Qualifications

Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)

Knowledge of machine learning concepts and their application to reasoning and problem-solving

Experience in Python, Perl, or another scripting language

Experience in a ML or data scientist role with a large technology company

Experience in defining and creating benchmarks for assessing GenAI model performance

Experience working on multi-team, cross-disciplinary projects

Experience applying quantitative analysis to solve business problems and making data-driven business decisions

Experience effectively communicating complex concepts through written and verbal communication

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