Amazon's Sourcing team - part of the Supply Chain Optimization Technologies (SCOT) organization - develops and deploys machine learning models to help Amazon's businesses worldwide anticipate supplier performance as we order and stock the right inventory for our customers. We are seeking a Data Scientist with strong machine learning, analytical, communication, and project management skills to join our team. As part of multi-skilled team engineering teams, and with product owners, you will help identify and solve complex prediction problems to improve inbound supply chain systems modelling supplier behaviour so we can offer products to our customers with confidence we will deliver on time. Also, you will develop metrics and reports to measure our impact on Amazon's business. In a typical day, you will work closely with retail leads, product managers, engineers, and various business groups.
You are an individual with outstanding analytical and machine learning modelling abilities, excellent communication skills, good business understanding, and technically savvy. The successful candidate will be an analytical problem solver who enjoys diving into data, is excited about investigations and algorithms, can multi-task, and can credibly interface between technical teams and business stakeholders.
•Collaborating with colleagues from multidisciplinary science, engineering, and business backgrounds, to identify and reduce gaps in current inventory availability.
•Build statistical, predictive and machine learning models to drive sourcing accuracy.
•Ensure data quality throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, and transformation.
•Promote data-driven decision making across the team, using tools such as Tableau, SQL, and Excel.
•Influence leaders to make thoughtful data decisions to provide the most consistent and usable data for all teams across the company.
•Combine expert knowledge of statistical methodology and analysis with advanced programming skills to support complex analyzes.
•Share/develop best practices with other Amazon teams on a global scale.
•Build metrics, reports and dashboard to analyze key inputs to SCOT systems controlling buying and availability.
•Bachelor's degree or higher in an analytical area such as Machine learning, Computer Science, Physics, Mathematics, Statistics, Engineering or similar.
•4+ years professional experience in Analytics or other quantitative disciplines.
•Experience with modeling and analysis including machine learning, statistical analysis, operations research and management science and data mining.
•Strong interpersonal and communication skills. Must be able to explain technical concepts and analyses implications clearly to a wide audience, and be able to translate business objectives into actionable analyses.
•Experience with SQL (any variations thereof), Python/PySpark and/or Scala preferred; though experience with other analytics software (SAS, STATA, MATLAB, Mathematica, R/SparklyR) is acceptable.
•Familiarity with AWS solutions such as Glue, S3, and Redshift.
•Proven analytical and quantitative skills to use hard data and metrics to back up assumptions, develop business cases, and complete root cause analyses.
•Capable of taking responsibility for an initiative and working with minimal direction; self-starter even when assignments are vague or undefined.
•Creative in finding new solutions/designing innovative methods, systems, and processes.
•Graduate degree (MS or equivalent) or higher in a quantitative field such as Mathematics, Statistics, Analytics or Economics.
•Knowledge and experience with Agile development practices.
•Experience in Demand Planning & Forecasting, Supply Chain or Inventory Management is a plus.
•Demonstrated ability to manage multiple competing priorities simultaneously and drive projects to completion.
•Excellent written / oral communication and interpersonal skills.
•6+ years professional experience in Analytics or other quantitative disciplines.
Posted: October 20, 2018
Closes: December 19, 2018