Data Science Manager, PXT Central Science

Job Description

Locations

• United States, WA, Bellevue, Bellevue, WA, United States
• United States, WA, Seattle, Seattle, WA, United States
• United States, VA, Arlington, Arlington, VA, United States

Salary

Salary not disclosed

Skills Required

• - 5+ years of building quantitative solutions as a scientist or science manager experienceintermediate
• - 2+ years of scientists or machine learning engineers management experienceintermediate
• - 5+ years of applying statistical models for large-scale application and building automated analytical systems experienceintermediate
• - Knowledge of Python or R or other scripting languageintermediate
• - Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJSintermediate
• - Experience in Database like NoSQL, or experience in SQL Server/MySQL and experience in software developmentintermediate
• - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissionsintermediate
• - Experience with data modeling, warehousing and building ETL pipelinesintermediate

Required Qualifications

• - 5+ years of building quantitative solutions as a scientist or science manager experience (experience, 5 years)
• - 2+ years of scientists or machine learning engineers management experience (experience, 2 years)
• - 5+ years of applying statistical models for large-scale application and building automated analytical systems experience (experience, 5 years)
• - PhD in computer science, mathematics, statistics, machine learning or equivalent quantitative field (degree in computer science)
• - Knowledge of Python or R or other scripting language (experience)
• - Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS (experience)
• - Experience in Database like NoSQL, or experience in SQL Server/MySQL and experience in software development (experience)
• - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions (experience)
• - Experience with data modeling, warehousing and building ETL pipelines (experience)

Preferred Qualifications

• - Experience with one of the following areas: machine learning technologies, Reinforcement Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP) or related applications (experience)
• - Experience with big data technologies such as: Hadoop, Hive, Spark, EMR (experience)
• - Experience operating large data warehouses (experience)
• - Experience hiring, developing, and managing high-performing technical teams (experience)
• Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $142,300/year in our lowest geographic market up to $272,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site. (experience)

Responsibilities

• - Leadership & Team Management: Independently manage and develop a diverse science team, creating an environment that enables consistent delivery and innovation; Build and maintain a high-performing team that can operate effectively and autonomously; Drive strategic growth opportunities for team members, providing paths to demonstrate higher-level scope, impact, and leadership; Establish clear performance metrics and audit mechanisms to track and communicate team progress; Foster a team culture focused on bringing research to production and delivering customer value
• - Technical & Scientific Direction: Partner with stakeholders and leadership to define and execute the scientific vision for your team; Lead the development of structural and predictive models, leveraging emerging technologies and novel features; Drive the implementation of data science workflows and simulation frameworks; Bridge the gap between science, technology, and business requirements; Leverage the broader Amazon scientific community to enhance team capabilities and knowledge sharing
• - Strategic Planning & Execution: Define and maintain team structure, strategic direction, and owned technologies; Establish processes that enable consistent delivery and quality of scientific artifacts; Drive reasonable schedules and adjust priorities to ensure optimal outcomes; Create and implement audit mechanisms to track team performance against goals; Remove roadblocks and optimize team productivity
• - Communication & Influence: Create well-written documents to effectively communicate with technical and non-technical audiences; Influence science and analytics practices across the organization; Build strong partnerships with stakeholders across different business units; Present complex scientific findings to senior leadership; Drive adoption of best practices and innovative solutions
• About the team
• The Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, machine learning, and Generative AI to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science and UX to develop and deliver solutions that measurably achieve this goal.

Documents

• Accommodations Information (web)
• https://www.aboutamazon.com/workplace/employee-benefits (web)

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