Product Manager - Internal Data Processing & Automation

Location: Remote - US (EST preferred)

About MediaRadar

MediaRadar, now including the data and capabilities of Vivvix, powers the mission-critical marketing and sales decisions that drive competitive advantage. Our competitive advertising intelligence platform enables clients to achieve peak performance with always-on data and insights that span the media, creative, and business strategies of five million brands across 30+ media channels. By bringing the advertising past, present, and future into focus, our clients rapidly act on the competitive moves and emerging advertising trends impacting their business.

About the Role

MediaRadar is seeking a Product Manager to join our growing Product team and play a critical role in optimizing and automating our core internal data processing pipelines. This role will focus on developing and enhancing the tools and systems responsible for the capture, collection, matching, and metadata tagging of advertising occurrence data across Linear TV, CTV (Streaming + Live Events), Digital, and Social platforms. A key objective will be to leverage Machine Learning (ML) and Large Language Models (LLMs) to drive significant automation and efficiency gains in these complex processes.

As a Product Manager, you’ll be responsible for managing the product lifecycle of these internal tools—from deeply understanding existing processes and identifying bottlenecks, to defining requirements, leading development, and driving the adoption of automated solutions. You’ll work cross-functionally with engineering, data science, and operations teams to translate intricate data challenges into impactful product experiences, with a strong emphasis on practical execution and measurable improvements.

Requirements

Qualifications

What You’ve Done:

• 3–5 years of experience in a Product Management role at a SaaS or data-centric company, preferably with internal tools or B2B products.
• Demonstrated success in shipping features or products that have delivered measurable process improvements or business value, especially in data-intensive environments.
• Experience working closely with data scientists, ML engineers, and software engineers in an Agile development environment.
• Strong analytical curiosity and problem-solving skills, with a proven ability to dig into complex datasets, identify inefficiencies, and translate data insights into actionable improvements.
• Excellent written and verbal communication skills; able to clearly articulate complex technical concepts and data challenges to both technical and non-technical audiences.
• Familiarity with the advertising, media, or martech ecosystem, particularly regarding ad data collection and classification, is a strong plus.
• Experience with product analytics tools (e.g., Pendo, Mixpanel), project management systems (e.g., Jira), and data visualization tools is a plus.
• Exposure to or foundational understanding of Machine Learning and/or Large Language Model concepts and their application in automation.

Key Responsibilities:

What You’ll Do:

Strategy & Planning

• Define, prioritize, and own the roadmap for our internal data processing and automation tools, aligning closely with company goals to maximize operational efficiency and data quality.
• Leverage deep data analysis, process mapping, and collaboration with internal stakeholders (e.g., Data Operations, Data Science, Engineering) to identify high-impact opportunities for ML/LLM-driven automation.
• Develop clear business cases for new automation features and process improvements, including cost-benefit analyses and anticipated efficiency gains.

Product Development

• Write clear, precise product requirements and user stories for internal data processing tools, focusing on automation workflows, data pipeline enhancements, and ML/LLM integration.
• Lead sprint planning, backlog grooming, and standups, ensuring efficient execution and delivery of automated solutions.
• Collaborate closely with Data Science and Engineering teams to design, develop, and deploy robust and scalable ML/LLM-powered automation features.
• Manage timelines and scope to ensure on-time delivery of product releases, prioritizing practical, implementable solutions.

Stakeholder Collaboration

• Serve as the primary point of contact for internal data operations, engineering, and data science teams to understand their challenges, gather input, and communicate roadmap updates on internal tools.
• Partner with Data Operations and Quality Assurance teams to ensure successful deployment, validation, and ongoing optimization of a