Business Intelligence Engineer II

Job description

DESCRIPTION

Amazon.com’s Transaction Risk Management Services (TRMS) mission is to make Amazon the safest and most trusted place worldwide to transact online. Amazon runs one of the most dynamic e-commerce marketplaces in the world, with nearly 2 million sellers worldwide selling hundreds of millions of items in ten countries. TRMS safeguards every financial transaction across all Amazon sites. As such, TRMS designs and builds the software systems, risk models and operational processes that minimize risk and maximize trust in Amazon.com. TRMS organization is looking for a BIE for its Forecasting and Planning Research team. The team is being grown to provide insights about its TRMS planning and provide analytical solutions to help drive operational efficiencies, uncover the hidden risks and trends, reduce investigation errors and bad debt, improve customer experience and predict & recommend the optimizations for future state of TRMS operations.

As a BIE, you will be responsible for modeling complex problems, discovering insights and identifying opportunities through the use of statistical, machine learning, algorithmic, data mining and visualization techniques. You will need to collaborate effectively with internal stakeholders and cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards. The candidate should be able to apply a breadth of tools, data sources and analytical techniques to answer a wide range of high-impact business questions and present the insights in concise and effective manner. Additionally, the candidate should be an effective communicator capable of independently driving issues to resolution and communicating insights to non-technical audiences. This is a high impact role with goals that directly impacts the bottom line of the business.

Responsibilities:
· Use machine learning techniques to forecast TRMS investigations for improved long term and short term capacity planning.
· Employ the appropriate algorithms to discover patterns of risks, abuse and help reduce bad debt
· Design experiments, test hypotheses, and build actionable models to optimize TRMS operations
· Solve analytical problems, and effectively communicate methodologies and results
· Build predict models to forecast risks for product launches and operations and help predict workflow and capacity requirements for TRMS operations
· Draw inferences and conclusions, and create dashboards and visualizations of processed data, identify trends, anomalies
· Work closely with internal stakeholders such as business teams, engineering teams, and partner teams and align them with respect to your focus area

PREFERRED QUALIFICATIONS

· Experience in statistical techniques such as classification, clustering, regression, statistical inference, collaborative filtering, and natural language processing, experimental design, social networking analysis, feature engineering, etc.
· Experience in creating forecasting using time-series and causal models.
· Experience/knowledge of advanced machine learning techniques such as GBM, random forest, etc.
· Experience in e-commerce / on-line companies in fraud / risk control functions
· Coding skills in one of the modern languages Java, Python, R
· Experience with visualization technologies such as Tableau
· Compelling communication and influencing skills and participation in winning the support of management and influence the course of major strategic decisions

Desired profile

BASIC QUALIFICATIONS

· Bachelor’s Degree in any quantitative discipline such as Statistics, Mathematics, Quantitative Finance or Operational Research
· At least 3 years of experience working in Analytics / Business Intelligence environment
· Experience in working with databases and SQL in a business environment
· Demonstrated use of analytical packages and scripting languages such as R, Python, SAS and SPSS.
· Prior experience in design and execution of analytical projects
· Worked extensively in large scale data bases and data warehouses