Key Responsibilities
- Own product strategy for integrating telematics, AI/ML capabilities, and cross-platform data services to deliver predictive insights, fleet optimization, and real-time operational visibility.
- Drive execution of a Unified Data Layer, ensuring seamless interoperability between internal systems, customer environments, 3rd party ERP/TMS/WMS, and external APIs.
- Translate emerging AI technologies (e.g., anomaly detection, computer vision, NLP) into differentiated product features that address real-world customer problems.
- Define and maintain a multi-year product roadmap with strong focus on data infrastructure, algorithm-driven insights, and user-centered visualization experiences.
- Collaborate with engineering, data science, and DevOps teams on architecture, data pipelines, AI model integration, and scalability across markets.
- Conduct quarterly product reviews and customer advisory boards to ensure roadmaps reflect customer feedback and market trends.
- Manage products throughout the lifecycle, from ideation to go-to-market, applying agile practices and frameworks.
- Champion a culture of innovation, data-driven decision-making, and effective cross-functional collaboration.
Qualifications
- 6+ years in product management within transportation, logistics, or enterprise SaaS.
- Maters Degree
- Proven experience working with AI-powered products, connected data platforms, or ML model deployment in commercial environments.
- Expertise in defining product requirements for data ingestion pipelines, real-time analytics, and API-first architectures.
- Strong knowledge of data privacy, system integration, and compliance requirements in telematics or transportation tech.
- Ability to translate business goals and user needs into product specifications and technical delivery plans.
- Demonstrated ability to balance long-term vision with short-term execution in high-growth, agile environments.
- Experience collaborating with data science, AI/ML engineers, and cloud infrastructure teams to deliver scalable, intelligent systems.
- Excellent communication and storytelling skills for both technical and non-technical audiences.
- Strong business acumen with experience in KPI frameworks, ROI models, and performance tracking.
Nice to Have
- Experience with Jira, Confluence, or other agile lifecycle tools.
- Familiarity with big data systems, SQL/NoSQL, or streaming analytics platforms (e.g., Kafka, Spark).
- Exposure to AI technologies such as predictive maintenance, driver behavior analysis, computer vision for asset tracking, or NLP for service automation.
- Working knowledge of third-party data integrations, such as ELDs, OEM APIs, routing software, or insurance platforms.