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Sr. Manager, Applied Science, Seller Growth Business
4 months ago
We aim to accelerate the growth of Sellers by providing tools, insights, and recommendations that enable them to make better and faster decisions at each step of selection management. To accomplish this, we offer intelligent insights that are both detailed and actionable, allowing Sellers to introduce new products and engage with customers effectively. We leverage extensive structured and unstructured data to generate science-based insights about their business. Furthermore, we provide personalized recommendations tailored to individual Sellers' business objectives in a user-friendly format. These insights and recommendations are integrated into our products, including Amazon Brand Analytics (ABA), Product Opportunity Explorer (OX), and Manage Your Growth (MYG). Additionally, we leverage the start-of-art technology, such as Gen AI to enable efficient insights consumption and recommendation digestion for Sellers, as well as providing an interactive experiences to Sellers using our tools.
We are looking for a talented and passionate Sr. Applied Science manager to lead our research endeavors and develop world-class statistical and machine learning models. The successful candidate will work closely with Product Managers (PM), User Experience (UX) designers, engineering teams, and Seller Growth Consulting teams to provide actionable insights that drive improvements in Seller businesses.
Key job responsibilities
Your responsibilities will include:
•Taking full service ownership of your teams including developing research and engineering (product roadmaps), developing and tracking relevant metrics, and ensuring system performance.
•Ensuring the team are publishing high-quality research in top conferences and journals, which are also directly impactful for our customers.
•Delivering against project plans and program commitments.
•Managing the day-to-day activities of a combined science and engineering team within an Agile environment, but paying attention to the different timescales of development.
•Hiring research scientist and engineers to continue to grow the team.
•Coaching and developing scientists to maintain standards and to drive career growth for your direct reports.
•Building a team culture around technical excellence and strong delivery.
About the team
The Seller Growth science team aims to provide data and science solutions to drive Seller growth and create better Seller experiences. We structure our science domain with three key themes and two horizontal components. We discover the opportunity space by identifying opportunities with unrealized potential, then generate actionable analytics to identify high value actions (HVAs) that unlock the opportunity space, and finally, empower Sellers with personalized Growth Plans and differentiated treatment that help them realize their potential.
We are open to hiring candidates to work out of one of the following locations:
New York City, NY, USA | Seattle, WA, USA
BASIC QUALIFICATIONS
•Ph.D. degree in Computer Science, Computer Engineering, Machine Learning, or related field•Typically requires 10+ years of relevant work experience
•Excellent publication record of ML and NLP at major conferences that shows breadth and depth
•Recognized as a leader in area of specialty (machine learning, Natural Language Processing, etc.)
•Proven track record of mentoring and developing other researchers
•Strongly desirable to be proficient in more than one more major programming languages (C++, Java, C#, C, Perl/Ruby, etc.)
•Expert in some mathematical programming libraries (such as R, Weka, Matlab, SAS)
•Expert knowledge in model evaluation and performance, operationalization and scalability of scientific techniques and establishing decision strategies.
PREFERRED QUALIFICATIONS
•Industry experience showing research results being deployed at scale•Experience building and delivering models for complex systems that leverage our technology platform and work well with other company systems
•Specific leadership in e-commerce, handling highly ambiguous problems, and Gen AI
