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- Tasks: Join us to tackle real-world problems using AI and machine learning!
- Company: Be part of Amazon, a leader in innovation and customer-centric solutions.
- Benefits: Enjoy competitive pay, remote work options, and a vibrant company culture.
- Why this job: Make an impact with cutting-edge technology while collaborating with talented teams.
- Qualifications: 5+ years in data science, SQL, Python, and statistical modeling required.
- Other info: Opportunity to mentor and lead within a dynamic data science team.
The predicted salary is between 43200 - 72000 £ per year.
Amazon strives to be Earth’s most customer-centric company where people can find and discover virtually anything they want to buy online. Amazon’s evolution is driven by the spirit of innovation that is part of the company’s DNA.
Amazon Seller Services is looking for a Data Scientist to work hands on from concept to delivery on generative AI, statistical analysis, prescriptive and predictive analysis, and machine learning implementation projects. We are looking for a problem solver with strong analytical skills and a solid understanding of statistics & machine learning algorithms as well as a practical understanding of collecting, assembling, cleaning and setting up disparate data from enterprise systems.
Key Job Responsibilities
- Ability to understand a business problem and the available data and identify what statistical or ML techniques can be applied to answer a business question.
- Given a business problem, estimate solution feasibility and potential approaches based on available data.
- Understand what data is available, where, and how to pull it together. Work with partner teams where needed to facilitate permissions and acquisition of required data.
- Quickly prototype solutions and build models to test feasibility of solution approach.
- Build statistical models/ML models, train and test them to drive towards the optimal level of model performance.
- Improve existing processes with development and implementation of state of the art generative AI models.
- Work with technology teams to integrate models by wrapping them as services that plug into Amazon’s marketplace and fulfillment systems.
- Work across the spectrum of reporting and data visualization, statistical modeling and supervised learning tools and techniques and apply the right level of solution to the right problem.
- The problem set covers aspects of detecting fraud and abuse, improving performance, driving lift and adoption, recommending the right upsell to the right audience, cost saving, selection economics and several others.
BASIC QUALIFICATIONS
- 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience.
- 5+ years of data scientist experience.
- Experience with statistical models e.g. multinomial logistic regression.
PREFERRED QUALIFICATIONS
- Experience working with data engineers and business intelligence engineers collaboratively.
- Experience managing data pipelines.
- Experience as a leader and mentor on a data science team.
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.
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Sr. Data Scientist, FCGT employer: Amazon
Contact Detail:
Amazon Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Sr. Data Scientist, FCGT
✨Tip Number 1
Familiarize yourself with Amazon's culture and values, especially their focus on customer-centricity and innovation. This understanding will help you align your problem-solving approach with what they value most.
✨Tip Number 2
Brush up on your skills in SQL and Python, as these are crucial for the role. Consider working on personal projects or contributing to open-source projects that showcase your ability to handle data querying and scripting.
✨Tip Number 3
Network with current or former Amazon employees, especially those in data science roles. They can provide insights into the interview process and the types of projects you might work on.
✨Tip Number 4
Prepare to discuss specific examples of how you've applied statistical models and machine learning techniques in past projects. Be ready to explain your thought process and the impact of your work.
We think you need these skills to ace Sr. Data Scientist, FCGT
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and qualifications required for the Sr. Data Scientist position at Amazon. Tailor your application to highlight relevant experiences that align with the job description.
Highlight Technical Skills: Emphasize your proficiency in data querying languages like SQL, scripting languages such as Python, and any experience with statistical software. Provide specific examples of projects where you've applied these skills effectively.
Showcase Problem-Solving Abilities: In your application, include examples of how you've approached complex business problems using statistical or machine learning techniques. Detail the methods you used and the outcomes achieved to demonstrate your analytical capabilities.
Collaborative Experience: Mention any experience working collaboratively with data engineers and business intelligence teams. Highlight your role in managing data pipelines and how you contributed to team success, as this is crucial for the position.
How to prepare for a job interview at Amazon
✨Showcase Your Analytical Skills
Be prepared to discuss specific examples of how you've applied statistical and machine learning techniques to solve business problems. Highlight your experience with data querying languages like SQL and scripting languages such as Python.
✨Demonstrate Problem-Solving Abilities
During the interview, focus on your approach to understanding business problems and how you identify the right statistical or ML techniques to apply. Be ready to walk through your thought process in tackling complex data challenges.
✨Discuss Collaboration Experience
Emphasize your experience working with data engineers and business intelligence teams. Share examples of successful collaborations that led to improved data pipelines or model implementations.
✨Prepare for Technical Questions
Expect technical questions related to statistical models and machine learning algorithms. Brush up on topics like multinomial logistic regression and be ready to explain how you've used these models in past projects.
