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תל־אביב–יפו, מחוז תל אביב, ישראל
Hybrid
DV is the leader in digital performance solutions, helping our advertiser and agency partners verify the quality of their digital campaigns, Optimise to improve performance and Prove that they’re achieving their business outcomes, through unbiased 3rd party data and analytics. DV’s mission is to be the definitive source of transparency and data-driven insights into the quality and effectiveness of digital advertising for the world’s largest brands, agencies, publishers, and digital ad platforms. Since 2008, DV has helped hundreds of Fortune 500 companies gain the most from their media spend by delivering best-in-class solutions across the digital advertising ecosystem, helping to build a better industry. Learn more at www.doubleverify.com.
You will lead a team of experienced Data Scientists while remaining deeply involved in the technical work.
This is a hands-on leadership role (~70% hands-on) combining direct modeling work with ownership of team direction and execution.
You will work on core systems that operate at a massive scale, where:
Data is abundant, but labels are scarce and expensive
problems are long-tail and ambiguous
Systems must meet strict latency and cost constraints (pre-bid)
Your responsibilities include:
Lead development of content classification systems across social platforms (Meta, TikTok, YouTube), web, and apps
Design and build models across computer vision, NLP, and multimodal pipelines
Own the full lifecycle: data selection -> labeling strategy -> training -> evaluation -> deployment
Develop strategies for efficient data curation and labeling (active learning, auto-labeling, sampling under scale)
Improve model quality (precision/recall) while balancing cost, latency, and scale
Drive automation systems (auto-labeling, auto-curation, retraining loops)
Apply modern AI approaches (LLMs, embeddings, foundation models) to real production problems
Lead and mentor a team of senior Data Scientists, setting technical direction and pushing execution forward
Work closely with ML Engineering, Product, and Policy to translate ambiguous requirements into scalable systems
3+ years of experience leading Data Science / ML teams
6+ years of hands-on experience in Machine Learning / Deep Learning
Strong background in Computer Vision and/or NLP
Experience building and deploying production ML systems at scale
Strong understanding of real-world trade-offs (accuracy, cost, latency)
Technical requirements
Hands-on experience with deep learning frameworks (PyTorch / TensorFlow)
Experience with ML/DS tools (scikit-learn, OpenCV, HuggingFace, etc.)
Experience working with large datasets and model evaluation pipelines
Advantages
Experience with multimodal systems (vision + text + audio)
Experience with LLMs / embeddings / foundation models
Experience with AutoML, active learning, or data-centric AI