|Country||United States of America|
As Amazon's WWOperations organization continues to grow, talent planning and forecasting is increasingly essential to our decision making processes across the most critical initiatives. We forecast out for tens of thousands of hires a year, and talent plan for many more. This Data Science Manager on the Talent Planning and Forecasting team will be driving world wide initiatives to discover and enable hiring at the right time, place, and the right people.
In this role you will closely partner with the WWOperations, HR, and TA leadership teams. You will also interact with various engineering, data, and science teams across the company to discover best in class approaches to forecasting talent movement and enabling this to become an actionable plan.
We are looking for a Science Manager who can build and lead a team in a very visible organization. Not only will this person have the opportunity to develop their team, but also will be a thought leader within the Talent Forecasting space. Collaboration across engineering, research, and business teams will be paramount for future innovation. Finally, this person will be an expert at synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication
• A highly talented technical leader with 5+ years of hands-on experience as a scientist or science manager in building quantitative solutions
• 2+ years of experience managing teams of Machine Learning, Data Science and/or Engineering professionals
• 2+ years of experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), or statistical/mathematical software (e.g. R, SAS, Matlab)
• Advance degree (MS, PhD) in Computer Science, Statistics, Machine Learning, Applied Math or a related field.
• Depth and breadth in quantitative knowledge. Excellent quantitative modeling, statistical analysis skills and problem-solving skills. Sophisticated user of statistical tools.
• Excellent written communication skills with the ability to effectively advocate technical solutions to research scientists, engineering teams and business audiences
• Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
• Experience in People/HR Analytics or Supply Chain is a plus
• Experience articulating business questions and using quantitative techniques to arrive at a solution using available data
• Have the ability to explain analytical concepts to senior leadership and to non-technical audience is a must