Product Manager - Digital Advertising

Principal Product Manager, CTV App Security & Data IntelligencePlease note: Only candidates currently residing in Singapore with valid work authorization are eligible for this roleAbout the RolePixalate is seeking a Principal Product Manager to lead our Connected TV (CTV) App Security and Data Intelligence product suite. You'll drive strategy, development, and GTM execution for products protecting the rapidly growing CTV advertising ecosystem from sophisticated fraud schemes, device/app spoofing, SSAI manipulation, and invalid traffic. This highly technical role requires deep expertise in CTV platforms, programmatic advertising, fraud detection architectures, and analyzing massive datasets to identify emerging CTV threats.As CTV ad spend exceeds $30 billion, your work will be critical in establishing trust and transparency in this high-growth channel.Remote position based in Singapore.Key ResponsibilitiesDrive product strategy and roadmap for CTV app security and data intelligence platform across Roku, Amazon Fire TV, Apple TV, Samsung Smart TV, and emerging platformsBuild deep expertise in CTV fraud: app/device spoofing, bundle ID fraud, bot-driven fraud, SSAI exploitation, and proxy IVTWork with vast CTV data (billions of monthly programmatic ad impressions, millions of apps, 300+ million OTT devices) to identify fraud patterns; use SQL extensively to analyze and validate detection algorithmsPartner with data scientists and PhD-level engineers on CTV-specific fraud detection algorithms and machine learning modelsMonitor CTV fraud landscape including 500K+ daily fraud sources and emerging attack vectorsOwn pricing, packaging, and GTM strategy for advertisers, publishers, DSPs, and SSPs; establish Pixalate as CTV security thought leaderBuild relationships with CTV platforms, media buyers, industry analysts, and trade publications; represent Pixalate at IAB Tech Lab, MRC, TAG, and CTV-specific initiativesEnsure products meet MRC accreditation standards for Sophisticated Invalid Traffic (SIVT) detection on CTVPerform market research with CTV ecosystem participants to evaluate highest ROI opportunitiesRequired QualificationsMaster's degree or PhD in Computer Science, AI, Machine Learning, Engineering, Math, Physics, or related quantitative/technical field3-5+ years B2B SaaS product management experience in ad tech, CTV, fraud detection, or verification productsDeep understanding of CTV ecosystem including major platforms (Roku, Fire TV, Apple TV, Samsung), app stores, and streaming servicesStrong knowledge of CTV fraud types and programmatic advertising (SSPs, DSPs, CTV supply chain)Very comfortable with SQL and querying data warehouses to analyze CTV traffic patterns and fraud indicatorsUnderstanding of CTV-specific technologies: Server-Side Ad Insertion (SSAI), VAST/VPAID, Open Measurement SDK, Device AttestationFamiliarity with ad fraud detection methodologies, machine learning for fraud detection, and MRC standardsExcellence in writing technical PRDs and security product specifications for ad techExceptional communication skills for technical and business audiences; experience with ad tech platforms and industry organizationsExtremely analytical mindset with passion for challenging the status quo in ad verificationPreferred QualificationsCTV advertising, programmatic media buying/selling, or ad verification experienceBackground in ad tech platforms (DSPs, SSPs, ad exchanges)Video advertising standards, brand safety, viewability, and ad fraud prevention experienceTrack record of CTV fraud reports, research publications, or speaking engagementsExperience with device manufacturers, OTT platforms, or streaming servicesHigh-growth ad tech startup experienceAbout PixalateWe're the leader in fraud detection and privacy compliance for digital advertising, protecting billions in ad spend for Fortune 500 brands and major ad platforms. As fraudsters increasingly use AI to create sophisticated attacks, we're building the next generation of AI-powered detection systems to stay ahead. Our platform has been used to unearth multiple high-profile criminal and illegal surveillance cases, including:

Created: 2025-12-03