Full Job Description
Key job responsibilities
• Frame loosely defined business problems as science problems, and iterate on the approach with limited guidance.
• Establish ground truth. Design the sampling, set the label quality bar, and build datasets across behavioral, text, and catalog domains.
• Define precision, recall, and coverage, and report what accuracy is achievable at what cost.
• Document failure modes, quantify the cost of each error type, and set thresholds with product and policy partners before deployment.
• Deliver methodology and analysis on medium-size projects, written so other scientists and business partners can both act on the results.
• Decide whether to train in-house, fine-tune, or call a language model, and make that call on evidence.
• Use generative AI tooling to scale your own throughput rather than only to build products.
• Ship solutions another scientist can test and reproduce.
• Partner with engineers to move models into production, then define drift monitoring and retraining triggers.
• Build deep knowledge of what the team owns, and mentor teammates on method choice.
A day in the life
Most of the week is modeling and measurement: pulling data, building features, running evaluations, and discussing thresholds with evidence rather than opinion. You use AI tooling to handle the repetitive parts, which keeps your focus on the actual science. The rest of your time goes to the product managers, policy partners, and operations team who act on your model's outputs every day. Because the team is small, you move between problem areas rather than owning just one.
BASIC QUALIFICATIONS
- 4+ 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 data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Bachelor's degree
- Experience applying theoretical models in an applied environment
- Knowledge of machine learning concepts and their application to reasoning and problem-solving
PREFERRED QUALIFICATIONS
- Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent)
- Experience in a ML or data scientist role with a large technology company
- Experience working for an entertainment agency, studio, or organization
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, SEATTLE - 136,000.00 - 184,000.00 USD annually
