DESCRIPTION

Passionate about helping customers simplify and accelerate their machine learning workloads on Sagemaker and AWS? Come join the Machine Learning Marketplace team and help us build the future of ML.

You'll work alongside engineers and leaders in both AWS Marketplace and Sagemaker. You will have deep understanding and experience building Machine Learning models and how to apply them in practice. You will invent new ways for sellers to package IP and enable buyers with the right capabilities to evaluate, experiment, re-train and integrate machine learning into their systems.

We are looking for highly motivated applied scientists and engineers interested to help us realize the vision of simple and effective ML.

Responsibilities include:
• Designing and implementing new features in Marketplace and SageMaker to enable IP packaging, training, evaluation.
• Work with Fortune 500 companies to understand use cases and help build prototypes and proof-of-concepts.
• Lead teams of engineers in building the latest ML features for marketplace
• Evaluate existing capabilities and provide guidance for ML best practices and state-of-the-art.
• Working closely with Product Managers to expand depth of our product insights with data, create a variety of experiments, and determine the highest-impact projects to include in planning roadmaps
• Providing technical and scientific guidance to your team members
• Communicating effectively with senior management as well as with colleagues from science, engineering, and business backgrounds
• Being a cultural leader that ensures teams are collecting, understanding, and using data to inform every decision that impacts our customers
The successful candidate will have an established background in developing customer-facing experiences, a strong technical ability, a start-up mentality, excellent project management skills, and great communication skills.

Inclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

Work/Life Balance
Our team puts a high value on work-life balance. It isn't about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.

Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.

BASIC QUALIFICATIONS

• PHD/MS. in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
• At least 5+ years of hands-on experience in predictive modeling and analysis
• Proficiency in model development, model validation and model implementation.
• At least 2+ years hands on experience programming in R, Java, C#, C++ or other similar programming languages
• Hands on experience with scripting languages such as Python, Perl
• Work well in a fast-moving team environment and effectively deliver technical implementations having complex dependencies and requirements.

PREFERRED QUALIFICATIONS

• 7+ years of hands-on experience applying theoretical models in an applied environment
• Extensive knowledge and practical experience in several of the following areas: machine learning, statistics, deep learning, Natural Language Processing, recommendation systems, dialogue systems, informational retrieval
• Significant peer reviewed scientific contributions in premier journals and conferences
• Proven track in leading, mentoring, and growing teams of scientists
• Experience with digital media, online advertising or retail
• Proven ability to work effectively in a cross-functional team
• Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts

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. For individuals with disabilities who would like to request an accommodation, visit https://www.amazon.jobs/en/disability/us