Data Scientist

Job Description

At Amazon, we strive to be most customer-centric company on the earth. To get there, we need exceptionally talented, bright, dynamic and driven people to develop the next-generation technologies.Are you champion of innovating on behalf of the customer by turning data insights into action Do you want to be part of Amazon s strategic and highly impactful finance and payroll technology projects Amazon s Finance Automation Payroll Tech team has an opportunity for a seasoned Data Scientist whose experience illustrates a clear ability to create world class machine learning systems to meet long-term Payroll needs.At Finance Automation, we are passionate about building systems and services that deliver a seamless and transparent finance experience for Amazon partners. We build, operate, and scale systems that are responsible for billions of dollars in transactions, and are central to the success of worldwide finance. We think globally, act locally to revolutionize a worldwide employee and customer experience and fulfill our promise to pay accurately, on-time, with lowest cost to Amazon.As a Data Scientist at Finance Automation, you are self-driven leader with extensive experience in applying statistics and data science concepts to bring tangible benefits for global finance operations. Finance and Payroll domain knowledge is a plus but not required. Work with finance and payroll stakeholders to understand the organization goals, objectives, and pain points. Identify key areas to drive Machine Learning initiatives, define needle-moving business questions and success criteria. Collect and analyze finance, HR, and payroll data across multiple isolated systems. Derive actionable insights from large volumes of heterogeneous data. Create reliable and maintainable code to build regression, classification, clustering, and anomaly detection systems. Work closely with software engineers to develop data ingestion and visualization, and productionize your models. Partner with finance analysts, and payroll managers to deploy and test machine learning systems with your statistical models at the core. Automate feedback loops, tune, and improve the models in production. Train non-tech stakeholders and partners to effectively use your machine learning systems. Set the right expectations on model limitations and prove business value. Create and maintain business and technical artifacts such as requirements documentation, use cases, performance evaluation, and model metrics. Learn and utilize AWS technologies and Amazon machine learning systems to effectively work with terabytes of data.Being part of Amazon Finance Automation gives you the opportunity to work in a rapidly growing organization with many high performing global technology teams. Come join us in making history!, Bachelors or Master s degree in Statistics, Applied Math, Operations Research, Economics, Engineering or a related quantitative field with 5 years of working experience as a Data Scientist. In-depth knowledge on supervised and unsupervised machine learning algorithms including classification, clustering, and regression. Experience building productions systems with statistical analysis, data modeling, regression modeling and forecasting, time series analysis, and deep learning neural networks. Expertise in coding using one or more programming languages such as R, Python, MATLAB, and Spark to build machine learning models. Skilled in manipulating and processing data using libraries such as Scikit-learn, Pandas, and NumPy. Demonstrated experience in SQL and/or NoSQL data modeling. Experience processing, filtering, and presenting large quantities of data. Ability to design for performance, scalability, and availability. Excellent communication, analytical and problem-solving skills. Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives. Obsession with quality, operational excellence, and customer experience. Ability to convey rigorous mathematical concepts and considerations to non-experts. Passion to dive deep to resolve problems at their root, looking for failure patterns amenable to long-term solutions via simplification and automation. Experience in AWS is a huge plus. Functional knowledge of AWS platforms such as Sagemaker, S3, Glue, Dynamodb, and RedShift. Exposure to finance and payments domain is a plus. Deep understanding of data, application, server, and network security. Experience with agile or scrum methodology.

Keyskills :
use casesanalytics time series analysisglobal finance deep learningmachine learning data analysistime series