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Data Scientist, Region Flexibility at Amazon

Amazon No longer available

Job Description

Job ID: Services LLC

The Region Flexibility Engineering (RFE) team builds and leverages foundational infrastructure capabilities, tools, and datasets needed to support the rapid global expansion of Amazon's SOA infrastructure. Our team focuses on robust and scalable architecture patterns and engineering best practices, driving adoption of ever-evolving and AWS technologies. RFE is looking for a passionate, results-oriented, inventive Data Scientist to refine and execute experiments towards our grand vision, influence and implement technical solutions for regional placement automation, cross-region libraries, and tooling useful for teams across Amazon.

As a Data Scientist in Region Flexibility, you will work to enable Amazon businesses to leverage new AWS regions and improve the efficiency and scale of our business. Our project spans across all of Amazon Stores, Digital and Others (SDO) Businesses and we work closely with AWS teams to advise them on SDO requirements. As innovators who embrace new technology, you will be empowered to choose the right highly scalable and available technology to solve complex problems and will directly influence product design. The end-state architecture will enable services to break region coupling while retaining the ability to keep critical business functions within a region. This architecture will improve customer latency through local affinity to compute resources and reduce the blast radius in case of region failures. We leverage off the sciences of data, information processing, machine learning, and generative AI to improve user experience, automation, service resilience, and operational efficiency.

Key job responsibilities

As an RFE Data Scientist, you will work closely with product and technical leaders throughout Amazon and will be responsible for influencing technical decisions and building data-driven automation capabilities in areas of development/modeling that you identify as critical future region flexibility offerings. You will identify both enablers and blockers of adoption for region flex, and build models to raise the bar in terms of understanding questions related to data set and service relationships and predict the impact of region changes and provide offerings to mitigate that impact.

About the team

The Regional Flexibility Engineering (RFE) organization supports the rapid global expansion of Amazon's infrastructure. Our projects support Amazon businesses like Stores, Alexa, Kindle, and Prime Video. We drive adoption of ever-evolving and AWS and non-AWS technologies, and work closely with AWS teams to improve AWS public offerings. Our organization focuses on robust and scalable solutions, simple to use, and delivered with engineering best practices. We leverage and build foundational infrastructure capabilities, tools, and datasets that enable Amazon teams to delight our customers. With millions of people using Amazon's products every day, we appreciate the importance of making our solutions "just work".

Basic Qualifications
  • 2+ years of data scientist experience
  • 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, and parameters that affect their performance experience
  • Experience applying theoretical models in an applied environment
Preferred Qualifications
  • Experience in Python, Perl, or another scripting language
  • Experience in a ML or data scientist role with a large technology company

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, 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 for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $125,500/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit . This position will remain posted until filled. Applicants should apply via our internal or external career site.

Location: ES, Community of Madrid, Madrid.

Typical mid-level pay: $113k for Data Scientists nationally

National salary averages
Expected mid-level
$113k
Entry
Mid
Senior
Expected
$64k Market range (10th-90th percentile) $194k

Top performers earn significantly more—skill and negotiation matter.

Senior roles pay 89% more than entry—experience is well rewarded.

Strong candidate leverage

High demand and responsive wages. Negotiate confidently on all fronts.

Hiring leverage
Candidate-favored
Wage leverage
Moderate
Mobility
Good mobility

Who this leverage applies to

Stronger for: All experience levels

Where to negotiate

Base salary
Sign-on bonus
Title / level
Remote flexibility
Scope & responsibility
Start date / PTO

Likely Possible Unlikely

Use competing offers and timing to your advantage.

Does this path compound?

Job Growth →
High churn
Growth, flat pay
🚀 Compound
Growth + pay upside
⚠️ Plateau
Limited growth
Specialize
Experts earn more
Pay Upside →
Growth + pay upside

Both the field and your earnings can grow significantly.

+34%
10yr growth
New positions are being created faster than they're being filled.
A bachelor's degree is typically expected.
Typical: Bachelor's degree

Good time to build expertise—demand will chase supply.

Labor data: BLS 2024