Data Scientist, AWS Professional Services Israel

Key Skills

Aartificial intelligence, AWS, Data science techniques, Deep Learning, Effective communication skills, Generative AI, Keras software library, Machine learning techniques, PyTorch, TensorFlow

Job Description

You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.

We’re looking for ML Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.

Key job responsibilities

• Collaborate with ML scientist and architects to Research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges.
• Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production.
• Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder.
• Provide customer and market feedback to Product and Engineering teams to help define product direction.

About the team

The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently.

Diverse Experiences

Amazon values diverse experiences. Even if you do not meet all of the preferred 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.

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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

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 and 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.

BASIC QUALIFICATIONS

• Masters degree (or European advanced degree equivalent) in Computer Science, or related technical, math, or scientific field
• Relevant experience in building large scale machine learning or deep learning models and solutions
• Experience communicating across technical and non-technical audiences
• Experience in using Python and hands on experience building models with deep learning frameworks like Tensorflow, Keras, PyTorch, MXNet
• Fluency in written and spoken Hebrew and English

PREFERRED QUALIFICATIONS

• Proven knowledge of Generative AI and hands-on experience of building applications with large foundation models
• Proven knowledge of AWS platform and tools
• PhD degree in Computer Science, or related technical, math, or scientific field
• Hands-on experience of building ML solutions on AWS