Data Scientist, IMDb
DESCRIPTION
IMDb is the world's most popular and authoritative source for information on movies, TV shows, and celebrities. Products and services to help fans discover and decide what to watch and where to watch it include: the IMDb website for desktop and mobile devices; apps for iOS and Android; and, X-Ray on Prime Video. For entertainment industry professionals, IMDb provides IMDbPro and Box Office Mojo. IMDb licenses information to third-party businesses worldwide. As an Amazon company, IMDb employees enjoy the benefits and resources of a tech giant, while maintaining the autonomy and impact of a small, nimble team.
Key job responsibilities
Ready to revolutionize advertising in the entertainment industry? We’re seeking a talented Data Scientist, to lead game-changing projects that will transform how fans interact with ads. Your work will drive IMDb's success, while improving the experience for our diverse set of global customers - from passionate film fans to entertainment industry insiders.
In this role, you'll drive innovative projects across IMDb, collaborating with diverse teams to shape the future of entertainment technology. We're seeking candidates who blend advanced machine learning expertise with a genuine passion for entertainment and data-driven innovation. You'll develop sophisticated ML models and leverage generative AI technologies to revolutionize digital advertising, enhance personalization, and create next-generation fraud detection systems. Your work will transform customer experiences through scientific methods, deep learning architectures, and state-of-the-art AI models. 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.
BASIC QUALIFICATIONS
- 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
- 2+ years of data scientist 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
PREFERRED QUALIFICATIONS
- Experience in Python, Perl, or another scripting language
- Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
- Experience implementing algorithms using both toolkits and self-developed code
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, 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 https://amazon.jobs/content/en/how-we-hire/accommodations 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 https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.