Senior Product Manager, ML Models
New
J
JobgetherMaritime awareness
Work remotely from the United States.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- At least 5 years of experience in product management, program management, or technical leadership, including 2 or more years taking technical products from conception to launch.
- Required Skills
- PythonSQLMachine LearningProduct ManagementCross-functional Team LeadershipA/B testing
Requirements
- Bachelor's degree in engineering, computer science, or a related technical discipline, or equivalent practical experience.
- At least 5 years of experience in product management, program management, or technical leadership.
- At least 2 years taking technical products from conception to launch.
- Experience defining requirements, success metrics, and evaluation approaches for machine learning or AI-powered products in collaboration with ML engineers and data scientists.
- Familiarity with A/B testing, offline model evaluation, and data-driven experimentation methods.
- Working proficiency in Python and SQL.
- Hands-on experience or strong familiarity with predictive modeling, recommendation or ranking systems, or LLM and agentic pipelines.
- Ability to lead engineers, researchers, designers, and operational teams without direct reporting authority.
- Ability to explain technical concepts, brief senior stakeholders, and influence product and business decisions.
- Experience or familiarity with maritime, defense, aerospace, or national security environments.
- Strong prioritization, analytical reasoning, decision-making, and problem-solving skills focused on measurable outcomes and product quality.
- Comfort with remote collaboration, flexible working hours, and occasional periods requiring increased availability.
Responsibilities
- Engage with users, analysts, and partners to understand maritime activity needs and translate findings into prioritized problems, user journeys, and success criteria.
- Establish the model portfolio and roadmap, including capability priorities and performance standards.
- Define model metrics, acceptable error rates, latency and edge-computing constraints, data requirements, and evaluation datasets.
- Collaborate with Applied Machine Learning and Radio Frequency Machine Learning engineers on experiments, evaluation methods, error analysis, and release criteria.
- Assess model performance and guide iterative improvements until models meet deployment requirements.
- Present roadmaps, technical trade-offs, risks, and performance results to leadership, customers, and strategic partners.
- Lead model initiatives from concept through development, launch, deployment, and ongoing performance monitoring.
- Connect field performance and user feedback to data collection, labeling, model retraining, and product iterations.
- Develop metrics, experimentation practices, and evaluation frameworks for model development.
- Align machine learning, software, hardware, data, and operational teams around technical roadmaps and product objectives.
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