DESCRIPTION

Amazon’s FinOps Global Data Analytics’s (GDA) science team seeks an outstanding Data Scientist with the technical expertise and business intuition to invent the future of Accounts Receivable and Account Payable at Amazon. As a key member of the science team, the Data Scientist will own high-visibility analyses, methodology, and algorithms in the Order-to-Cash (O2C) and Procure-to-Pay lifecycles 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, credit risk, cash flow, and operational efficiencies.

Key job responsibilities
The Data Scientist’s responsibilities include, but are not limited to the following points:
– Extract and analyze large amounts of data from Order-to-Cash and Procure-to-Pay processes 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 business data and 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 and data science 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 leveraging 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 across Amazon’s numerous businesses, 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 India, US, 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

– 2+ years of data scientist experience
– 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
– 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
– 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
– Knowledge of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.