Staff Data Scientist

5 days ago

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Integral Ad Science

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A leading global media measurement and optimization platform.

Media quality • Digital fraud detection and blocking • Ad verification • Ad viewability • Advertising data

501 - 1000

Description

• Scope and Develop ML Projects: Lead the identification and execution of machine learning projects • Data Integration and Model Development: Integrate diverse data sources to build and deploy production-ready ML models and recommendation systems that enhance ad targeting, placement, and performance. • Innovative Solutions: Apply machine learning to novel problems in programmatic advertising, pushing the boundaries of what’s possible. • Cross-Functional Collaboration: Work closely with engineers and commercial teams to seamlessly integrate ML solutions into broader engineering workflows, ensuring alignment with business objectives. • Leadership in Model Development: Spearhead the development of proprietary AI models tailored for programmatic ad optimization, directly contributing to revenue-generating products. • Stakeholder Communication: Effectively communicate insights from ML models to various stakeholders, including R&D, product teams, and commercial partners, ensuring that technical results translate into actionable business strategies. • Domain Expertise: Establish yourself as a domain expert in programmatic advertising, leading research initiatives and overseeing the end-to-end delivery of client-facing products.

Requirements

• Advanced degree (MSc, PhD preferred) in a relevant technical field (machine learning, computer science, statistics, mathematics etc) • 8+ years of industry experience, with technical leadership, and experience guiding ML projects from start to finish • Experience with at least one scripting language such as Python • Familiarity with relational databases and SQL • Experience with source control workflows and deploying ML models in production • Comfort manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources • Experience distilling product requirements into problem definitions, dealing with ambiguity and competing objectives • Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner • Deep curiosity and a love of problem solving • Practical experience with programmatic advertising and the bid stream • Experience in building recommendation systems • Experience working with distributed computing tools (Hadoop, Spark, Hive, etc.) and cloud platforms (AWS, GCP)