Data Curation Specialist

Who we are DoubleVerify is a big data and analytics company. We track and analyze tens of billions of ads every day for the biggest brands in the world like Apple, Nike, AT&T, Disney, Vodafone, and most of the Fortune 500 companies. If you ever saw an Ad online via Web, Mobile, or CTV device then there are big chances that it was analyzed and tracked by us.  We operate at a massive scale, our backend handles over 100B+ events per day, we analyze and process those events in real-time while making decisions on the environment where the ad is running and all the user interactions during the Ad display lifecycle. We verify that all Ads are Fraud Free, Brand Safe, in the right Geo and highly likely to be viewed and engaged, all that in less than a fraction of a second. We are global, we have R&D centers in Tel Aviv, New York, Finland, Belgium and San Diego, we work in a fast-paced environment and have a lot of challenges to solve. If you like to solve big data challenges and want to help us build a better industry then your place is with us.   What will you do? You will have an in-depth understanding of our content classification policies, and based on that you will collect, organize, filter and improve data sets of images, videos, texts and audio that are used for training and testing our machine learning classification models. You will work with local and global teams that are responsible for policy, labeling and annotation to make sure we have accurately labeled datasets.   Requirements: Graduates of Social/Psychology/Communication studies, or a related field. Excellent attention to detail and accuracy in data review and curation. Excellent communication skills in English, both written and verbal, with the ability to clearly communicate data quality issues to both technical and non-technical stakeholders. Strong organizational skills, proficiency in Excel.  Job Responsibilities: Curate, review and validate datasets for accuracy and completeness. Identify and report data quality issues and work with cross-functional teams to resolve them. Collaborate with content stakeholders to ensure adherence to data quality standards and guidelines. Develop and maintain quality control procedures to ensure consistent data quality. Analyze and interpret data to identify trends and insights that can be used to improve data quality. Document data quality issues, findings, and recommendations for future reference.