Stackadapt
Technical Product Manager, Mobile App Program
productfull-timeWorldwide
SALARY
Not specified
WORK TYPE
remote
JOB TYPE
full-time
INDUSTRY
ai
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About the role
Headquarters: United States StackAdapt is the leading technology company that empowers marketers to reach, engage, and convert audiences with precision. With 465 billion automated optimizations per second, the AI-powered StackAdapt Marketing Platform seamlessly connects brand and performance marketing to drive measurable results across the entire customer journey. The most forward-thinking marketers choose StackAdapt to orchestrate high-impact campaigns across programmatic advertising and marketing channels. What you'll be doing Technical Collaboration: Work closely with data science and machine learning engineers to develop and implement advanced models and algorithms that optimize ad performance and user acquisition. Infrastructure Integration: Collaborate with the engineering team to ensure seamless integration of existing ad infrastructure, including bidding, serving, pacing, etc. Stakeholder Communication: Communicate clearly and precisely with stakeholders across the organization, ensuring everyone is informed and aligned at the right times. Data-Driven Decisions: Utilize analytical skills to define key success metrics, monitor KPIs, and make data-driven decisions that drive product success. Industry Expertise : Stay ahead of industry trends, competitive landscape, and evolving market needs, aligning these insights with StackAdapt's mission and vision. What you bring to the table Educational Background: Bachelor's degree in Computer Science, Engineering, Data Science, or a related field. A Master's degree is a plus. Experience: 3-5 years of technical product management experience with a proven track record of delivering impactful projects in the advertising technology space. AdTech Knowledge: Proficiency in Ad servers, bidding, auctions, DSPs, SSPs, and MMPs. Experience with building CTR and CVR-type models. Technical Expertise: Deep understanding of ads infrastructure, including bidding and pacing, as well as experience with machine learning models (e.g., XGB
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