Amazon Finance Operations Global Data Analytics (GDA) science team seeks a Sr. 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 highvisibility analyses methodology and algorithms in the ProcuretoPay 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 tradeoffs between customer experience cash flow and operational efficiencies in our payment processes.
Key job responsibilities
The Sr. Data Scientists responsibilities include but are not limited to the following points:
Manage relationships 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.
Define the vision for data science in the accounts payable space in partnership with process and technology leaders.
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 finetuning model parameters. Review and recommend improvements to science models and architecture as they relate to accounts payable process and tools.
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. Serve as a point of contact for questions from business and operations leaders.
Develop code to analyze data (SQL PySpark Scala etc. and build statistical and machine learning models and algorithms (Python R Scala etc..
A day in the life
As a successful data scientist in GDAs Science team you will dive deep on data from Amazons payment practices and customer support functions extract new assets drive investigations and algorithm development and interface with technical and nontechnical 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 selfservice standard reporting; and build predictive analytics. Our topmost ambition is to actively contribute to the improvement of Amazons Free Cash Flow by valueadding analytics. Our success is built on users trust in our data and the reliability of our analytics tools. GDAs data scientists and economists further that mission with rigorous statistical econometric and ML models to compliment reporting and analysis developed by GDAs analytical BI and Finance professionals.
5 years of data querying languages (e.g. SQL) scripting languages (e.g. Python) or statistical/mathematical software (e.g. R SAS Matlab etc. experience
4 years of data scientist experience
5 years of data scientist or similar role involving data extraction analysis statistical modeling and communication experience
3 years of data visualization using AWS QuickSight Tableau R Shiny etc. experience
Experience managing data pipelines
Experience as a leader and mentor on a data science team
Masters degree in a quantitative field such as statistics mathematics data science business analytics economics finance engineering or computer science
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