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Senior Manager, Product Management , Seattle, Washington

CategoryProject/Program/Product Management--Non-Tech
Job typeFull Time
CountryUnited States of America
Amazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy and sell online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment.
What's a long-standing challenge of our global product and operations teams? Scalable metrics measurement and expeditious decision making through systems. Our operations have evolved over time and currently we are making tech investments to scale the cradle to grave system that enable our global operations team to respond to our customers. A significant part of this scaling is currently to apply decision making for global workload distribution, prioritization model for blending different types of work globally, prioritizing line leader tasks for automation etc. Each of these decisions are based on creating close loop systems across all systems that carry changes in supply and demand. As our supply and demand systems become high grain and sophisticated, the decision to alter either side will require a need to build recommendations to changes into long-term, short term forecasting of work, supply management [real time/long term (training, skilling, hiring)] and recommendation to change customer promise (SLA/shutdown of contact method).
In this role, you will work directly with operations, program, product and tech resources to develop and execute a cross-team and cross-functional recommendation system. This roadmap will deliver a close loop within different production systems (at scale), recommending actions to match supply and demand. You'll shape our strategy in a rapidly evolving landscape. With stakeholders across multiple teams, you'll have frequent opportunities to influence significant decisions.
The Sr. Manager will work closely with other analytic teams, data engineers, research scientists, machine-learning experts, and economists to collate current decisions (systemic or manual). They will connect the data flow across the systems that enable the decisions today. Then look at our OP1 and 3YP investments that influence how these will evolve to come up with a roadmap to create recommendation at ends of different operations systems. These would span across demand forecasting, routing of work, micro-systems of, leave, attendance, adherence and training support. The Sr. Manager will collaborate with technology and product leaders to solve business and technology problems using approaches to build new services that surprise and delight our customers.
A successful candidate will be able to understand and manage key operational and technical concepts. They will have excellent project and communication skills, and motivation to achieve results in a fast-paced environment. Candidates should demonstrate a passion for working on behalf of customers, have a record of accomplishment of timely delivery of large-scale projects, and have the ability to influence multiple global teams. Autonomy, judgment, influence, and leadership skills are essential. This person will be responsible for ensuring we meet our key deliverables, on time with high quality, and communicating status to internal and external stakeholders.

The key strategic objectives for this role include:
• Understanding drivers, impacts, and key influences on decision-making within different each sub-process.
• Optimizing work to drive decision-making automation.
• Helping to build production systems that take inputs from multiple systems and make decisions in real time.
• Automating feedback loops for algorithms in production.
• Utilizing Amazon systems and tools to effectively work with terabytes of data.
What types of problems will they solve?
• Demand and Supply systems coordination- demand, supply and operational hand offs between teams generate a lot of ebbs and flows in our systems. Work force manages supply and demand over a 1 week+ horizon and RTA manages it at a 30 min interval, each using different systems. Inputs from these different systems and knowledge of supply levers drives the decisions to change capacity. The solution figures information needed to co-ordinate planning; allocation, hiring and staffing decisions and where we need real-time handshakes between these systems to enable that. It also requires figuring out the right interfaces to co-ordinate these activities and where to create linkages across 2 systems.
• Unify Ticket data sourcing and processing - Today our ticket/async call data sourced from different systems and it is generated by each workflow or SOP usage by associate. How do we source the data that exists in different systems, and encrypt, process and classify in a way we can consume it a single ticketing UI by linking Atlas label to SOP to workflow to ticket tree.
• Fatigue driven by repetitive tasks - How we solve for fatigue/disengagement that will be driven by repetitive task and what is the best way to model handing this work to associates to prevent this.

Basic Qualifications:
Basic Qualifications
• 5+ years of experience working with data science, engineering and production systems in a consumer product company, Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
• Ability to understanding technical production systems, how they interact with each other and grasp information flow amongst systems.
• Ability to apply data information and system knowledge to solve the specific business problems.
• Awareness and ability to stay abreast of external efforts (outside PSAS) related to the problem at hand.
• Extensive knowledge and practical experience in several of the following areas: machine-learning, statistics, recommendation systems.
• Ability to manage and quantify improvement in customer experience or value for the business resulting from adding multiple data teams
• Superior verbal and written communication skills, ability to convey rigorous mathematical concepts and considerations to non-experts.

Preferred Qualifications:
Preferred Qualifications
• Masters in quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent)
• Extensive knowledge and practical experience in several of the following areas: machine-learning, statistics, NLP, deep learning, recommendation systems, dialogue systems, information retrieval.
• Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
• Demonstrated industry leadership in the fields of Database and/or Data Warehousing, Data Sciences and Big Data processing.

Amazon.com is an Equal Opportunity-Affirmative Action Employer - Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.

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