Job ID: 2916032 | AWS EMEA SARL (UK Branch) - F93 Are
you looking to work at the forefront of Machine Learning and AI?
Would you be excited to apply cutting edge Generative AI algorithms
to solve real world problems with significant impact? The AWS
Industries Team at AWS helps AWS customers implement Generative AI
solutions and realize transformational business opportunities for
AWS customers in the most strategic industry verticals. This is a
team of data scientists, engineers, and architects working
step-by-step with customers to build bespoke solutions that harness
the power of generative AI. The team helps customers imagine and
scope the use cases that will create the greatest value for their
businesses, select and train and fine tune the right models, define
paths to navigate technical or business challenges, develop
proof-of-concepts, and build applications to launch these solutions
at scale. The AWS Industries team provides guidance and implements
best practices for applying generative AI responsibly and cost
efficiently. 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. Key job responsibilities 1.
Collaborate with AI/ML scientists, engineers, and architects to
research, design, develop, and evaluate cutting-edge generative AI
algorithms and build ML systems to address real-world challenges.
2. 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. 3.
Create and deliver best practice recommendations, tutorials, blog
posts, publications, sample code, and presentations adapted to
technical, business, and executive stakeholder. 4. Provide customer
and market feedback to Product and Engineering teams to help define
product direction. About the team 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 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. 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 1. 2+ years of
data scientist experience and 3+ years of data querying languages
(e.g. SQL), scripting languages (e.g. Python) or
statistical/mathematical software (e.g. R, SAS, Matlab, etc.)
experience. 2. 3+ years of machine learning/statistical modeling
data analysis tools and techniques, and parameters that affect
their performance experience. 3. Experience applying theoretical
models in an applied environment. 4. Bachelor's degree in a
quantitative field such as statistics, mathematics, data science,
business analytics, economics, finance, engineering, or computer
science. PREFERRED QUALIFICATIONS 1. PhD in a quantitative field
such as statistics, mathematics, data science, business analytics,
economics, finance, engineering, or computer science. 2. 5+ years
of machine learning/statistical modeling data analysis tools and
techniques, and parameters that affect their performance
experience. 3. Hands-on experience with deep learning (e.g., CNN,
RNN, LSTM, Transformer). 4. Prior experience in training and
fine-tuning of Large Language Models (LLMs) and knowledge of AWS
platform and tools. Amazon is an equal opportunities employer. We
believe passionately that employing a diverse workforce is central
to our success. We make recruiting decisions based on your
experience and skills. We value your passion to discover, invent,
simplify and build. Protecting your privacy and the security of
your data is a longstanding top priority for Amazon. Please consult
our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to
know more about how we collect, use and transfer the personal data
of our candidates. 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. 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/accommodationsfor more
information. If the country/region you’re applying in isn’t listed,
please contact your Recruiting Partner. Posted: March 6, 2025
(Updated about 4 hours ago) #J-18808-Ljbffr
