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 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 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.
In this Data Scientist role, you will be capable of using GenAI and other techniques to design, evangelize, and implement and scale cutting-edge solutions for never-before-solved problems.
Key job responsibilities
- 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.
- Interact with customers directly to understand the business problem, assist them in the implementation of generative AI solutions, deliver briefing and deep dive sessions, and guide customers on adoption patterns and paths to production.
- Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholders.
- Provide customer and market feedback to Product and Engineering teams to help define product direction.
BASIC QUALIFICATIONS
- 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.
- 3+ years of machine learning/statistical modeling data analysis tools and techniques experience.
- Experience applying theoretical models in an applied environment.
- Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science.
PREFERRED QUALIFICATIONS
- PhD in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science.
- 5+ years of machine learning/statistical modeling data analysis tools and techniques experience.
- Hands-on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer).
- 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. 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.
Posted:April 3, 2024 (Updated 4 days ago)
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