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- Tasks: Tackle exciting business challenges using econometrics and machine learning to influence Amazon's product assortment.
- Company: Join Amazon, a global leader in innovation and technology.
- Benefits: Competitive salary, diverse work culture, and opportunities for professional growth.
- Other info: Inclusive environment that values diversity and encourages innovative thinking.
- Why this job: Make a real impact on global business decisions while working with top-tier professionals.
- Qualifications: PhD in relevant fields and experience in causal modeling and data science.
The predicted salary is between 60000 - 80000 £ per year.
We are looking for a Senior Economist to work on exciting and challenging business problems related to Amazon Retail’s worldwide product assortment. You will build innovative solutions based on econometrics, machine learning, and experimentation. You will be part of an interdisciplinary team of economists, product managers, engineers, and scientists, and your work will influence finance and business decisions affecting Amazon’s vast product assortment globally.
If you have an entrepreneurial spirit, you know how to deliver results fast, and you have a deeply quantitative, highly innovative approach to solving problems, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you.
Key job responsibilities- Work on a challenging problem that has the potential to significantly impact Amazon’s business position.
- Develop econometric models and experiments to measure the customer and financial impact of Amazon’s product assortment.
- Collaborate with other scientists at Amazon to deliver measurable progress and change.
- Influence business leaders based on empirical findings.
- PhD in business economics, engineering, analytics, mathematics, statistics, information technology or equivalent.
- Experience in causal modeling like graphical models, causal Bayesian network, potential outcomes, A/B testing, experiments, quasi-experiments, and data science workflows.
- Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives.
- Experience communicating results to senior leadership, or experience in building financial and operational reports/data sets that inform business decision-making.
- Experience conducting research on a global scale.
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.
Senior Economist , Economic Decision Science employer: Amazon Science
Contact Detail:
Amazon Science Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Economist , Economic Decision Science
✨Tip Number 1
Network like a pro! Reach out to current or former employees at Amazon, especially those in similar roles. A friendly chat can give you insider info and might even lead to a referral.
✨Tip Number 2
Prepare for the interview by brushing up on your econometrics and machine learning skills. Be ready to discuss how you've tackled complex problems in the past and how you can apply that to Amazon's product assortment.
✨Tip Number 3
Showcase your entrepreneurial spirit! Think of innovative solutions to hypothetical scenarios they might present during the interview. This will demonstrate your ability to think outside the box and deliver results fast.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re serious about joining the team.
We think you need these skills to ace Senior Economist , Economic Decision Science
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your econometrics and data science skills in your application. We want to see how you can apply these to solve real-world problems, so don’t hold back on showcasing your experience with causal modelling and A/B testing!
Tailor Your Application: Take the time to customise your CV and cover letter for this role. We’re looking for someone who understands the unique challenges of Amazon Retail, so make it clear how your background aligns with our needs and the exciting work we do.
Be Clear and Concise: When writing your application, keep it straightforward and to the point. We appreciate clarity, so avoid jargon and focus on communicating your ideas effectively. Remember, we want to understand your thought process and how you tackle complex problems!
Apply Through Our Website: Don’t forget to submit your application through our official website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Amazon Science
✨Know Your Econometrics
Brush up on your econometric models and causal analysis techniques. Be ready to discuss specific examples of how you've applied these methods in past projects, especially in relation to A/B testing or experiments. This will show that you can hit the ground running.
✨Showcase Your Collaboration Skills
Since you'll be working with a diverse team, highlight your experience collaborating with economists, product managers, and engineers. Prepare anecdotes that demonstrate your ability to communicate complex ideas clearly and work towards common goals.
✨Quantify Your Impact
Be prepared to discuss how your previous work has influenced business decisions. Use metrics and data to illustrate the impact of your econometric models or experiments. This will help the interviewers see the tangible value you can bring to Amazon.
✨Embrace Ambiguity
The job description mentions dealing with ambiguity and competing objectives. Think of examples where you've successfully navigated uncertain situations or conflicting priorities. This will show that you're adaptable and can thrive in a fast-paced environment.