Applied Scientist, AWS Marketing AI/ML Full-time

Amazon Web Services (AWS) is building a world-class marketing organization, and we are looking for an experienced Applied Scientist to join the central data and science organization for AWS Marketing. You will lead AWS personalization, targeting, and lead prioritization related AI/ML products and initiatives, and own mechanisms to raise the science and measurement standard. You will work with scientists, economists, and engineers within the team, and partner with product and business teams across AWS Marketing to build the next generation marketing generative AI and machine learning capabilities directly leading to improvements in our key performance metrics.

A successful candidate has an entrepreneurial spirit and wants to make a big impact on AWS growth. You will develop strong working relationships and thrive in a collaborative team environment. You will work closely with business leaders, scientists, and engineers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. The ideal candidate will have experience with machine learning architectures and models. Additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment. You will work on high-impact, high-visibility products, with your work improving the experience of AWS leads and customers.

AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector.

Key job responsibilities

* Lead the design, development, deployment, and innovation of advanced science models in the strategic area of marketing measurement and optimization.

* Partner with scientists, economists, engineers, and product leaders to break down complex business problems into science approaches.

* Understand and mine the large amount of data, prototype and implement new learning algorithms and prediction techniques to improve long-term causal estimation approaches.

* Design, build, and deploy effective and innovative ML solutions to improve components of our ML and causal inference pipelines.

* Publish and present your work at internal and external scientific venues in the fields of ML and causal inference.

* Influence long-term science initiatives and mentor other scientists across AWS.

A day in the life

Diverse Experiences

AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Why AWS?

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating – that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Inclusive Team Culture

Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.

Mentorship & Career Growth

We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Work/Life Balance

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

BASIC QUALIFICATIONS

– 3+ years of building models for business application experience

– PhD, or Master’s degree and 4+ years of CS, CE, ML or related field experience

– Experience in patents or publications at top-tier peer-reviewed conferences or journals

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

– Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

PREFERRED QUALIFICATIONS

– Experience using Unix/Linux

– Experience in professional software development

– Knowledge of architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members

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, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/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.