Senior Product Manager, ML Models
New
Q
Quartermaster AIMaritime AI
Location: United States
Workplace: RemoteFull-TimeManager
Salary not disclosed
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Job Details
- Experience
- 5+ years of experience in product management, program management, or technical leadership roles, including at least 2 years taking technical products from conception to launch.
- Required Skills
- PythonSQLMachine LearningProduct ManagementTensorflowA/B testingscikit-learn
Requirements
- 5+ years of experience in product management, program management, or technical leadership roles.
- At least 2 years taking technical products from conception to launch.
- Experience defining requirements, success metrics, and evaluation approaches for ML or AI-powered products.
- Experience working directly with ML engineers and data scientists.
- Experience running experiments and using data to drive product decisions, such as A/B testing or offline evaluation.
- Demonstrated ability to lead cross-functional teams of engineers, researchers, designers, and operators without direct authority.
- Track record of briefing senior leaders, including executives or customers, on complex technical topics and shaping decisions.
- Working fluency in Python and SQL to analyze data and engage in technical discussions with engineers.
- Bachelor's degree in engineering, computer science, or a related technical field, or equivalent practical experience.
- Preferred: MS in an engineering or technical discipline.
- Hands-on applied ML experience, such as predictive modeling, recommendation or ranking systems, or LLM/agentic pipelines; familiarity with scikit-learn or TensorFlow.
- Background or familiarity with maritime, defense, aerospace, or national security environments.
Responsibilities
- Understand how users, analysts, and partners detect, track, and make decisions about maritime activity.
- Turn customer insights into prioritized problem statements, user journeys, and measurable success criteria.
- Define the model portfolio, roadmap, requirements, target metrics, error modes, latency and compute budgets, data needs, and evaluation datasets.
- Work with Applied ML and RF ML Engineers on experiment design, evaluation methodology, error analysis, and release criteria.
- Communicate model strategy, progress, tradeoffs, and risks to executives, customers, and partners.
- Own execution from ideation through launch and ongoing performance monitoring.
- Build feedback loops from fielded systems into data collection, labeling, and retraining.
- Establish metrics, experimentation practices, and evaluation standards for model development.
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