Staff Product Manager, Machine Learning
New
O
OverstoryVegetation intelligence
Location: United States; Secondary Locations: Estonia, Sweden, Canada, Ireland, France, Portugal, United Kingdom, Netherlands, Denmark, Switzerland; Workplace: Remote, Europe (GMT/WET, CET, EET) and Eastern North America (NST, AST, EST, CST); comfortable maintaining 3-4 hours of overlap with European working hours.Full-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 8+ years of product management experience
- Required Skills
- Machine LearningProduct ManagementComputer Vision
Requirements
- 8+ years of product management experience, with a track record of product craft and shipping speed.
- Hands-on experience building and shipping products powered by machine learning models.
- Understand how machine learning models are built, trained, evaluated, and improved.
- Be able to discuss model performance and trade-offs with data scientists and ML engineers.
- Experience turning a scaling- or technical-led roadmap into a customer-driven roadmap, including user research and translating findings into priorities.
- Experience working in a fast-paced startup and making decisions amid ambiguity.
- Track record building B2B products customers rely on daily, including products that turn data or model outputs into actionable decisions.
- Strong written and verbal communication, including explaining technical trade-offs and shifting priorities.
- Experience leading product strategy and roadmapping and building consensus across product, engineering, and data science.
- Nice to have: experience building geospatial products or working with GIS technologies.
- Nice to have: experience with complex data products, data pipelines, platforms, and processing large, diverse datasets from multiple sources.
- Nice to have: experience building machine-learning products for image-based applications such as computer vision or image recognition.
Responsibilities
- Own the Vegetation Intelligence roadmap, spanning ML models that produce risk data and products that turn it into customer action.
- Advance detection models and help incorporate their outputs into broader outage and ignition risk models.
- Run user research and ground product priorities in customers’ operational decisions.
- Shift the roadmap from scaling- and technical-led to customer-driven while maintaining technical rigor.
- Work with data scientists and ML engineers to turn model capabilities into shipped product value.
- Define what good model performance looks like in production.
- Explore how feedback from customers and end users, including arborists, can improve model performance.
- Communicate model trade-offs, limitations, and priorities to product and engineering teams.
- Contribute to broader product strategy.
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