Data Scientist II, FinOps - Global Data Analytics

This job is with Amazon, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.

Description

Amazon Finance Operations Global Data Analytics (GDA) science team seeks a Data Scientist with the technical expertise and business intuition to invent the future of Accounts Payable at Amazon. As a key member of the science team, the Data Scientist will own high-visibility analyses, methodology, and algorithms in the Procure-to-Pay lifecycle to drive free cash flow improvements for Amazon Finance Operations. This is a unique opportunity in a growing data science and economics team with a charter to optimize operations and planning with complex trade-offs between customer experience, cash flow, and operational efficiencies in our payment processes.

Key job responsibilities

The Data Scientist's responsibilities include, but are not limited to the following points:

• Extract and analyze large amounts of data related to suppliers and associated business functions.
• Adapt statistical and machine learning methodologies for Finance Operations by developing and testing models, running computational experiments, and fine-tuning model parameters.
• Use computational methods to identify relationships between data and business outcomes, define outliers and anomalies, and justify those outcomes to business customers.
• Communicate verbally and in writing to business customers with various levels of technical knowledge, educate stakeholders on our research, data science, and ML practice, and deliver actionable insights and recommendations
• Develop code to analyze data (SQL, PySpark, Scala, etc.) and build statistical and machine learning models and algorithms (Python, R, Scala, etc.).
• Collaborate with business and operational stakeholders and product managers to innovate on behalf of customers, develop novel applications data science methodologies, and partner with engineers and scientists to design, develop, and scale machine learning models.
  
  

A day in the life

As a successful data scientist in GDA's Science team, you will dive deep on data from Amazon's payment practices and customer support functions, extract new assets, drive investigations and algorithm development, and interface with technical and non-technical customers. You will leverage your data science expertise and communication skills to pivot between delivering science solutions, translating knowledge of finance and operational processes into models, and communicating insights and recommendations to audiences of varying levels of technical sophistication in support of specific business questions, root cause analysis, planning, and innovation for the future. The role will work in a genuinely global environment, across various functional teams; with daily interaction across North America and Europe.

About The Team

Global Data Analytics (GDA) supports decisions in AR and AP. In close cooperation with our stakeholders, we agree and build uniform metrics; use data from a 'single source of truth'; provide automated, self-service, standard reporting; and build predictive analytics. Our topmost ambition is to actively contribute to the improvement of Amazon's Free Cash Flow by value-adding analytics. Our success is built on users' trust in our data and the reliability of our analytics tools. GDA's data scientists and economists further that mission with rigorous statistical, econometric, and ML models to compliment reporting and analysis developed by GDA's analytical, BI, and Finance professionals.

Basic Qualifications

• 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 5+ years of data scientist experience
• 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
• Bachelor's degree
• Experience applying theoretical models in an applied environment
  
  

Preferred Qualifications

• Experience in Python, Perl, or another scripting language
• Experience in a ML or data scientist role with a large technology company
  
  

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