Easter Seals Jobs

Description

We are a dynamic team of innovators passionately applying leading-edge technology to transform our customers' experiences with Amazon Devices. As a Senior Applied Scientist, you'll develop scientific models and tools, with a strong emphasis on time series forecasting, that enable business leaders to make decisions informed by unbiased, data-driven evidence. Your innovative solutions will directly influence millions of customers' experiences with Amazon Devices.

In this role, you'll tackle complex, unsolved problems at the intersection of machine learning, operations research, and business strategy. The challenges you'll solve include forecasting, recommendations, and classification, driving the long-term strategy for Amazon Devices Operations. Your models will operate at a global scale, optimizing operations across Amazon's vast network of devices and services.

Key job responsibilities

• Develop and implement innovative machine learning solutions: Create scalable, predictive models using advanced analytical techniques, with a focus on time series forecasting, to solve complex business problems and optimize key processes.

• Analyze and leverage large-scale data: Extract valuable insights from Amazon's vast historical business data to drive automation and optimization across various operations.

• Collaborate with cross-functional teams: Work closely with software engineering and operations teams to implement real-time models, create new features, and optimize business operations.

• Establish robust data science processes: Design and maintain scalable, efficient, and automated processes for time series data analysis, model development, validation, and implementation.

• Drive business impact through data-informed decisions: Conduct research on novel machine learning approaches, track business activities, and provide clear, compelling reports to management, ensuring alignment with Amazon Devices Operations' long-term strategic goals.

What We Offer:

• Access to state-of-the-art computing resources and vast datasets to support your research and development

• Significant opportunities for professional growth and the chance to become a thought leader in applied machine learning

• A diverse, world-class team of scientists and engineers, fostering an environment of continuous learning and innovation

• Opportunities to present your work at top-tier conferences

A day in the life

As you begin your day, you connect to your high-performance cloud services, leveraging state-of-the-art computing resources to run complex time series simulations on vast datasets. Your morning is spent analyzing results and extracting insights that could revolutionize Amazon Devices Operations. By lunchtime, you're collaborating with a diverse team of world-class scientists and engineers in a "lunch and learn" session, sharing knowledge and sparking innovative ideas. The afternoon involves mentoring a junior scientist, showcasing your commitment to fostering talent and contributing to the field's growth.

Later, you prepare for an upcoming presentation at a top-tier machine learning conference, excited to share your team's innovative work with the global AI community. Your day concludes with a cross-functional meeting, where you collaborate with engineers and product managers to apply leading-edge machine learning techniques to real-world challenges. Throughout the day, you've leveraged advanced resources, engaged in continuous learning, and pushed the boundaries of applied science - all while working on products that impact millions of customers. As you wrap up, you're energized by the significant opportunities for professional growth and the chance to become a thought leader in applied machine learning at Amazon Devices Operations.

About the team

Join our high-performing, innovative team that never settles for the status quo. As part of this dynamic organization, you'll work alongside a diverse group of research scientists, data engineers, user experience designers, product managers, and program managers - all passionate about continuously improving how things are done and taking immense pride in the impactful solutions we build. Our team is structured into related but divergent sub-groups, each focused on delivering business-driven software and data that address the evolving needs of Amazon's Devices Operations and Supply Chain.

If you thrive in a fast-paced, collaborative environment where innovation is the norm, you'll find yourself right at home here. We constantly push the boundaries of what's possible, leveraging state-of-the-art technologies and vast datasets to create transformative experiences for our customers. By providing an end-to-end perspective, we ensure the solutions we design and build today will solve both current and future challenges. Your contributions will make a tangible difference as we work together to set new standards in the industry and shape the future of Amazon Devices Operations.

Basic Qualifications

• 5+ years of building machine learning models for business application experience

• PhD, or Master's degree and 6+ years of applied research experience

• Experience with neural deep learning methods and machine learning

• Experience programming in Java, C++, Python or related language

• Experience in the following areas: time series forecasting, neural deep learning methods, and machine learning

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.

• Experience with large scale distributed systems such as Hadoop, Spark etc.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/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.