Recommended Machine learning Jobs 2025

Data Scientists-Recommended Machine learning Jobs 2025

Job Title: Senior Data Scientist – Amazon (Seattle, WA / New York, NY, USA)

Department: Data Science
Locations: Seattle, WA, USA or New York, NY, USA

About Amazon Data Science

At Amazon, data science isn’t a support function — it’s central to how we make decisions, optimize operations, and create better experiences for our customers. Amazon’s Data Science organization works on problems at enormous scale, from forecasting and recommendation systems to logistics optimization, advertising, and beyond. We work to reconcile strong scientific thinking with fast execution, driving the limits of what can be achieved in AI, ML, large‑scale systems, and applied modeling.

As part of this initiative, we are looking for a Senior Data Scientist to join one of our impactful teams. You will operate at the intersection of production and research, architecting models and solutions that power Amazon’s mainstays.

What You’ll Do (Key Responsibilities)

Oversee the end-to-end lifecycle of data science projects, from problem definition and framing to deployment, monitoring, and iteration.

Create new statistical, machine learning, and deep learning models to solve challenging business issues (e.g. demand forecasting, personalization, anomaly detection, optimization).

Process large-scale datasets (billions of rows) with distributed computing platforms (e.g. Spark, Redshift, Hive) and derive useful features, construct pipelines, and test hypotheses.

Work with software engineers, data engineers, product managers, and business stakeholders to deploy models into production platforms, making them scalable, robust, and maintainable.

Execute design and conduct experiments (causal inference or A/B) to test model performance and inform business strategy.

Operate model health after deployment, identify drift or performance decline, and retrain or update models accordingly.

Communicate insights, trade-offs, and important results to both technical and non-technical stakeholders, driving strategy and roadmaps.

Mentor less experienced data scientists and give back to the wider Amazon science community through knowledge sharing, code reviews, and internal papers.

A Day in the Life

You can start your day reading the most recent numbers out of a deployed forecasting model and monitoring for anomalies or drift. You get into feature engineering: trying out new signals, experiment with transformations, and working with data engineers to augment datasets. You sit down with a product manager and engineering team to establish logging requirements and API contracts for your models. You review findings from a new experiment, concluding whether or not a new variant of a model statistically beats the baseline. Lastly, you share your results with senior leadership, debating risk, tradeoffs, and follow-on steps.

Minimum Qualifications (Required)

Bachelor’s degree (or international equivalent) in quantitative discipline: Computer Science, Statistics, Mathematics, Engineering, Economics, or similar.

5+ years’ applied data science or quantitative modeling experience.

Proven experience querying large datasets with SQL, Hive, or similar technologies.

Strong Python (or R, Scala) programming skills in data processing, modeling, and analysis.

Hands-on experience in developing and validating models (statistical, machine learning, deep learning) and putting them into production environments.

Experience with distributed computing platforms (Spark, MapReduce, Redshift, etc.).

Very strong communication skills: capable of translating complicated analytical findings into simple, compelling stories for stakeholders.

History of creating measurable business results through data-driven decision-making.

Preferred Qualifications

Advanced level degree (Master’s or PhD) in a quantitative field.

Experience directly in demand forecasting, time series modeling, or supply chain analytics.

Understanding of causal inference methods, experimental design, uplift modeling.

Familiarity with AWS services (Sagemaker, Lambda, Glue, etc.).

Experience in handling streaming data and real-time analytics.

Publication track record or to the data science community (conferences, journals, open source).

Leadership or mentorship experience, including junior scientist coaching.

Team and Culture

You will be part of a cross-functional team of data scientists, data engineers, software engineers, and product leaders that operate in an agile, high-velocity environment. We foster curiosity, experimentation, and collaboration. Amazon scientists publish internally or externally regularly, and are nudged to remain at the forefront of methods. You’ll have access to the scale, diversity of domains, and massive compute and data resources of Amazon.

We also champion a culture of inclusion. Your voice counts. Different perspectives make for better solutions.

Compensation & Benefits-Recommended Machine learning Jobs 2025

Base salary range: $145,800/year (lowest-cost U.S. markets) to $294,700/year (highest-cost U.S. markets). (Pay is based on several factors such as geographic location, relevant skills, and experience.)

Amazon is a total compensation company. Beside base pay, candidates who are eligible may receive equity awards, sign-on bonuses, and other incentive compensation.

Complete set of medical, dental, vision, life insurance, disability pay, and retirement savings programs.

Paid family leave, paid vacation time, and company days off.

Training and development opportunities, such as internal courses, conferences, and tuition assistance.

Flexible work options (work from home or hybrid), based on team and function.

Career development resources, mentorship, and internal job opportunities.

To learn more about our employee benefits, go to: https://www.aboutamazon.com/workplace/employee-benefits

How to Apply & Timeline

This role will be open until filled. You need to apply through Amazon’s internal or external careers page. Make sure your resume indicates relevant modeling work, domain expertise (e.g., forecasting, optimization, experimentation), and quantifiable impact. We highly recommend inclusion of technical detail (e.g. code, model architectures, metrics) in addition to business context.

We generally work on this timeline:

• Resume & screening
• Technical phone screen(s) – including data science, coding, design, statistics
• Onsite or virtual interview loop – depth & breadth, case studies, behavioral (Amazon leadership principles)
• Final selection & offer
• We anticipate interviews will evaluate both your technical abilities and your fit with Amazon’s leadership principles.

Why Work Here?-Recommended Machine learning Jobs 2025

Here at Amazon, the scale and scope are huge. The systems and models you develop will shape how millions of customers shop, how supply chains are optimized, how advertising is spent, and much more. You’ll get to work with world-class engineering and scientific talent, massive data sets, and many tough, significant problems. You’ll advance your career, increase your impact across areas, and shape the future of data science at Amazon.

If scaling up, making real business impact, and collaborating with a passionate, diverse team energize you — we’d love to hear from you.

https://www.aboutamazon.com/workplace/employee-benefits