Manager, Data Science & Research
DoubleVerify - Tel Aviv-Israel
Posted Apr 12, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Manager, Data Science & Research Tel Aviv-Israel Who we are 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 . What you will do 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
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