Full-Time VP, Product Technology

We are excited to expand our business in the advanced advertising space with our Consumer Canvas dataset and MRI-Simmons truth set. We are dedicated to transforming the way marketers can activate and measure with more transparency and confidence.

The Product Technology leader will be responsible for driving the technical direction and engineering execution of our digital growth products.

This role requires a deep understanding of big data product management, big data processing, analytics & measurement, audiences, and adtech. The ideal candidate will have a proven track record of leading data product teams, data products involving data science, and developing scalable solutions, and delivering impactful results.

This role would report to the SVP of Product Strategy.

Key Responsibilities

• This role will be responsible for digital data products and technology necessary to shape our data and tech landscape in support of business goals in areas of Audiences, Activation, Measurement, Data Processing and Data & Insight Enablement.
• Lead Product Development: Guide and manage the team in the development of product features, ensuring they meet high-quality standards and align with the product roadmap. Make technical decisions that balance user needs, product goals, business objectives. Ensure the product is scalable, maintainable, and aligned with long-term strategy.
• Foster a culture of innovation, encouraging the team to explore new technologies, tools, and approaches that can enhance the product and development process. Promote a culture of experimentation, iteration, and learning within the team.

Technical Proficiency

Deep understanding of data product technologies, including databases, data analytics, machine learning, and cloud computing.

• Data Technologies: Understanding various types of databases (SQL, NoSQL), data warehousing solutions, data lakes, and big data technologies like Hadoop and Spark. Familiarity with data integration tools and ETL (Extract, Transform, Load) / ELT, are important
• Data Analytics and Visualization: Proficiency in data analytics tools (e.g., Python, R, SAS) and visualization platforms. This helps in interpreting data, generating insights, and presenting findings in a comprehensible manner.
• Machine Learning and AI: Knowledge of machine learning algorithms, frameworks (e.g., TensorFlow, PyTorch), and AI techniques.
• Cloud Computing: Understanding cloud platforms (e.g., AWS, Azure, Google Cloud) and their data services.
• Data Governance and Security: Awareness of data governance principles, data privacy laws and security best practices.
• Continuous Learning: Stay updated with the latest trends, tools, and technologies.

Communication Skills: Strong communication and interpersonal skills.

Team Management: Fostering a collaborative environment and supporting the professional development of team members.

Problem-Solving Skills: Identifying and solving problems quickly and efficiently.

Adaptability: Being adaptable and open to change.

Stakeholder Collaboration: Collaborate with cross-functional teams.

Business Acumen: Understanding the business side of technology.

Ethical Judgment: Ensuring data privacy and security.

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