Job Description

Job ID: 3132691 | Amazon.com Services LLC

We are a passionate team applying the latest advances in technology to solve real world challenges. As a Data Scientist working at the intersection of machine learning and advanced analytics, you will help develop innovative products that enhance customer experiences. Our team values intellectual curiosity while maintaining sharp focus on bringing products to market. Successful candidates demonstrate responsiveness, adaptability, and thrive in our open, collaborative, entrepreneurial environment.

Working at the forefront of academic and applied research, you will join a diverse team of scientists, engineers, and product managers to solve complex business and technology problems using scientific approaches. You will collaborate closely with other teams to implement innovative solutions and drive improvements.

At Amazon, we cultivate an inclusive culture through our Leadership Principles, which emphasize seeking diverse perspectives, continuous learning, and building trust. Our global community includes thirteen employeeled affinity groups with 40,000 members across 190 chapters, showcasing our commitment to embracing differences and fostering continuous learning through local, regional, and global programs.

We prioritize worklife balance, recognizing it as fundamental to longterm happiness and fulfillment. Our team is committed to supporting your career development through challenging projects, mentorship opportunities, and targeted training programs that help you reach your full potential.

Key job responsibilities

• Deliver data analyses that optimize overall team process and guide decisionmaking
• Deep dive to understand source of anomalies across a variety of datasets including lowlevel sequencing read data
• Identify key metrics that are drivers to achieve team goals; work with senior stakeholders to refine your results
• Use modern statistical methods to highlight insights for predictive & generative ML models and assay process
• Perform correlation analysis, significance testing, and simulation on high and lowfidelity datasets for various types of readouts
• Generate reports with tables and visualization that support operational cycle analysis and oneoff POC experiments
• Collaborate with multidisciplinary domain experts to support your findings and their experiments
• Write welltested scripts that can be promoted by our software teams to production pipelines
• Learn about new statistical methods for our domain and adopt them in your work
• Work fluently in SQL and Python. Be skilled in generating compelling visualizations.

A day in the life

New data has just landed and promoted to our datalake. You load the data and verify its overall integrity by visualizing variation across target subsets. You realize we may have made progress toward our goals and begin to test the validity of your nominal results. At midday you grab lunch with new coworkers and learn about their fields or weird interests (there are many). You generate visualizations for the entire dataset and perform significance tests that reinforce specific findings. You meet with peers in the afternoon to discuss your findings and breakdown the remaining tasks to finalize your group report!

About the team

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the companys Think Big leadership principle to the limits. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If youre interested in innovating at scale to address big challenges in the world, this is the team for you.

Basic Qualifications

• Ph.D. in computer science, engineering, mathematics or equivalent, or experience in data science, machine learning or data mining
• Experience analyzing noisy experimental data and implementing robust quality control methods
• Advance Knowledge in statistical analysis, hypothesis testing and sequential data analysis
• Experience with largescale data processing pipelines

Preferred Qualifications

• Expertise in applying machine learning algorithms to sequential pattern recognition
• Experience with computational modeling and optimization problems
• Track record of handling largescale, multidimensional datasets
• Familiarity with stateoftheart deep learning approaches including transformers and embedding models
• Publication record in leading machine learning or computational science venues
• Experience with automated systems and process optimization
• Knowledge of highperformance computing and distributed systems
• Experience in iterative experimental design and optimization
• Demonstrated ability to bridge theoretical models with experimental validation

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 youre applying in isnt 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 $143,300/year in our lowest geographic market up to $247,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on jobrelated knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, signon 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.

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