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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # IMDb Data Scientist, IMDb Insights and Analytics - **Company:** Amazon.com, Inc. - **Location:** Seattle, WA, United States - **Experience:** Experienced - **Salary:** $136,000.0 - $184,000.0 - **Contract:** Permanent contract - **Skills:** Data Analysis, Data Mining, Query Languages, R (Programming Language), Python (Programming Language), MATLAB, Machine Learning, Mathematical Software, SAS (Software), Software Engineering, SQL Databases, Scripting - **Published:** September 7, 2026 - **Apply:** https://dejobs.org/x/x/B18CA01379A9494F89A23AE7EB96443E/job/ ## About the Role We expect you to use frontier models to scale your own throughput, not only to embed them in products; fluency with these tools is part of the craft. Success in this position demands exceptional scientific leadership, strong organizational skills, software engineering excellence, product vision, and sharp business acumen, along with expertise in implementing responsible AI solutions., * 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, * 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 ## Description * 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. ## Related Videos - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [JavaScript? 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