Sr. Product Mgr - Tech, Amazon Leo Capacity Planning
Amazon Kuiper Commercial Services LLC•4h ago
United StatesOnsite$151.2K–$204.6KFull-timeSenior Level5+ yrs exp
H-1B verified · 2310 LCAs
Top focus
Vp ProductProduct ManagerProduct AnalystProduct OwnerTech Lead
- Amazon Leo is an initiative to increase global broadband access through a constellation of 3,236 satellites in low Earth orbit (LEO). Its mission is to bring fast, affordable broadband to unserved and underserved communities around the world. Amazon Leo will help close the digital divide by delivering fast, affordable broadband to a wide range of customers, including consumers, businesses, government agencies
- other organizations operating in places without reliable connectivity. As a Senior Product Manager–Technical on the Leo Capacity team, you will own products that connect measured network performance with capacity planning and network configuration decisions. You will partner with science, engineering, networking
- business operations teams to develop and productize monitoring, forecasting
- decision-support capabilities that use network performance actuals to identify emerging congestion risks, forecast performance trends
- explain differences between capacity-planning simulations and observed network outcomes. These capabilities will enable capacity planners and network operators to evaluate potential configuration changes, take action to meet performance targets
- continuously improve planning accuracy. Your work will help transform capacity planning from a reactive process into a proactive, data-driven system for network optimization at scale. The ideal candidate brings experience in technical product management with a focus on performance trend analysis, network optimization
- data-driven decision systems, ideally in satellite communications or another large-scale network domain. You should be comfortable working with complex time-series data, building analytical frameworks that compare simulation predictions against measured outcomes
- translating model outputs into actionable recommendations. You partner effectively across science, engineering, networking
- you thrive with ambiguity — defining strategy where the problem is understood but the solution requires invention. Key job responsibilities - Define and own the product vision for capacity performance monitoring and science-based forecasting systems that ingest network actuals to predict performance trends and identify emerging congestion risks days to weeks ahead at granular geographic levels. - Build feedback loops that surface gaps between planning simulation predictions and actual network performance, enabling root cause analysis and continuous improvement of planning accuracy, measured by forecast accuracy and reduced prediction-to-actual variance. - Partner with science and engineering teams to define requirements for models built on measured network data (supply, demand, utilization, service-level metrics) and evaluate proposed network configurations against capacity simulations, ensuring model outputs translate into actionable recommendations for configuration changes to meet performance targets. - Collaborate with cross-functional teams spanning capacity, networking
- business operations to develop and maintain the product roadmap, ensuring these performance forecasting and feedback systems are adopted into day-to-day planning and configuration workflows.
- Bachelor's degree - Experience owning/driving roadmap strategy and definition - Experience with feature delivery and tradeoffs of a product - Experience contributing to engineering discussions around technology decisions and strategy related to a product - Experience bridging technical and business teams to collect and refine requirements, prioritize incoming work requests
- ensure all committed work is delivered on time
- Experience building complex software systems that have been successfully delivered to customers
- experience designing or architecting (design patterns, reliability and scaling) of new and existing systems - Advanced degree in engineering, operations research, physics
- a related quantitative field - Experience validating or calibrating simulation, digital-twin
- forecasting models against measured outcomes - Experience working with cross-functional science and engineering teams to operationalize machine-learning models or decision engines - Experience with time-series trend analysis, anomaly detection
- performance optimization in network, cloud
- infrastructure domains - Experience with satellite communications and network-performance metrics such as throughput, latency, jitter, packet loss, link availability
- spectral efficiency Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability
- other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications
- location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off
- parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, WA, BELLEVUE - 151,200.00 - 204,600.00 USD annually
Required skills
product managementnetwork optimizationdata analysistime-series analysisanomaly detectionperformance optimizationmachine learningsatellite communications