Data Scientist - B2B Payments

Job description

DESCRIPTION

Looking for a career at a company that seeks to be Earth’s most customer-centric company? If so, meet Amazon.

Amazon's B2B Payments team is addressing the set of payment needs of the business customers as well as the financing needs of the seller businesses. Our mission is to create the most customer-centric payment products, payment experience and procurement services for business clients that empower any business to engage with Amazon from anywhere and in any way.

The Role
B2B Payments is seeking a Data Scientist who combines their technical expertise with business intuition. You will develop predictive models, design and measure tests, conduct causal studies to explain business-critical results, generate insights to set the strategic direction to enhance our product features & processes that will delight our customers.

Our ideal candidate thrives in a fast-paced environment, relishes working with large transaction volumes and big data, enjoys the challenge of highly complex business contexts (that are typically being defined in real-time). The candidate will be an expert in the areas of data science, machine learning, statistics and business analysis. In this role you will be expected to work with business leaders to understand the problems they need to solve and design the appropriate and intuitive solutions and by leveraging scientific methods.

Responsibilities
You will help create our data assets, then use necessary technical methods and conduct analyses to derive insights that are critical to business success. You will be responsible for researching insights as well as educating the business, product, marketing, and business development teams on the implementation of those insights to enable data-driven, day-to-day decision making. You will partner with our product management, marketing, engineering, operations and finance teams to:
· Contribute to the development and enhancement of business payment products and features.
· Use data mining, model building, and other analytical techniques to develop and maintain customer segmentation and predictive models to drive the business and improve our machine learning engine.
· Make recommendations for new metrics, techniques, and strategies to improve campaign targeting and measurement.
· Improve targeting capabilities and uncover hidden opportunities using data, analytics and machine learning.
· Understand business and product strategies, goals and objectives. Set the analytics roadmap to drive the goals of the business.
· Own the data-science scope for one or more product areas, lead planning, execution and delivery of projects
· Analyze and solve problems at their root, stepping back to understand the broader context.
· Interface with all internal related and ancillary teams to deliver data and analytics as requested.
· Provide support on experimental design, exploratory data analysis, and data management.

PREFERRED QUALIFICATIONS

· Graduate degree in Math, Finance, Economics, or Statistics or other related fields from an accredited university.
· Experience in payments, business analysis, strategic consulting, marketing, product management, credit risk or fraud risk.
· Familiarity with text mining, NLP and causal inference.
· Experience in complex data cleansing, data validation and master data management.
· Experience translating analysis results into business recommendations.
· Experience in using Python, R, SAS, SPSS, Matlab or other Statistical / Machine Learning Software.
· Advanced skills in data visualization tools like Quicksight, Tableau, Looker or similar BI tools.
· Hands on experience with statistical analysis and predictive modeling.
· Effective written and verbal communication skills.
Amazon is an Equal Opportunity Employer – Minority / Women / Disability / Veteran / Gender Identity / Sexual Orientation / Age.

Desired profile

BASIC QUALIFICATIONS

· Bachelor’s degree in a quantitative area such as math, statistics, computer science, engineering or equivalent experience.
· 3+ years of professional experience in a business environment or advanced degree in a quantitative field.
· Experience in A/B testing, statistics and model development.
· Proficient in using SQL, ETL, Data Warehouse solutions and databases in a business environment with large-scale, complex datasets.
· Ability to process large data sets from multiple data sources
· Ability to solve complex business problems.