Careers

Do you want to shape the battery energy storage technology at Amazon? We are looking for a Research Scientist to drive the technical evaluation of battery energy storage technology, from cells today to modules, systems and the technologies around them as our scope grows, and to be the technical interface with the suppliers and partners who work with us. You will conduct technical evaluations from test design through data analysis: help identify candidate technologies and supplies, engage their engineering teams, design test plans and protocol, coordinate sample sourcing and testing, and turn results into clear technical recommendations. You will visit supplier factories and laboratories, and you will work closely with your manager and colleagues across engineering, procurement, and business development, giving you direct exposure to how technical judgements become business decisions. This role suits someone who enjoys deep technical work and wants to grow into broader cross-functional influence. Key job responsibilities - Conduct technical evaluations of battery energy storage technologies, starting with lithium-ion cells and extending to modules, systems and related components: review specifications, test reports, safety certifications and manufacturing quality data, and identify gaps and risks. - Design test plans and protocols covering performance, cycle and calendar ageing, thermal characterisation and abuse/safety testing; propose acceptance criteria; and execute test programs under direction of Senior Scientist. - Coordinate the technical aspects of sample sourcing (specifications, shipping, storage, data management), partnering with Legal and Procurement through your manager - Process and analyse test data (capacity fade, resistance growth, efficiency, thermal behaviour) and build repeatable analysis pipelines and lifetime models. - Support supplier and partner engagements by contributing technical expertise in deep-dive sessions, factory visits, and quality assessments. - Prepare technical assessments, analysis summaries, and draft recommendations that feed into leadership reviews. Work with cross-functional colleagues, primarily in engineering and procurement, to move evaluations from technical findings toward decisions. - Travel frequently, domestically and internationally, for supplier and partner visits. A day in the life Your week mixes deep technical work with collaboration and learning. One day you might be reviewing a supplier's test data and specifications, turning an open question into a change to a running test plan. The next, you could be joining a technical deep-dive with a manufacturer's engineering team, or preparing observations and diligence items ahead of a factory visit. You will work closely with the laboratory on incoming results, support your manager on coordination with Legal and Procurement for agreements and samples, and partner with engineering colleagues to explore what the findings mean for system design. You will regularly prepare technical summaries and draft recommendations that feed into leadership discussions, and from time to time you will present your own analysis directly. Several times a year you will travel to a factory or laboratory in North America, Europe, or Asia. About the team We are a new team dedicated to Amazon's energy storage strategy. Our job is to give Amazon an independent, evidence-based view of energy storage technology: which cells and systems to trust, how long they will last, how they will fail and what they will cost. We work with suppliers and partners around the world and with colleagues across engineering, procurement, legal, finance and business development. The team is at an early stage and its scope is growing quickly, from cells today to modules, systems and the technologies around them. You will be an early hire, so you will help shape how we evaluate technology, how we engage suppliers and how we present results, with a direct line of sight to the decisions your work informs. For the right person this is a role with high growth potential and a wide range of opportunities as the team expands.

Join Amazon's Frontier AI & Robotics (FAR) team and play a hands-on, technically deep role in bringing the next generation of intelligent robotic systems to life. As a Robotics Lab & Support Engineer, you'll own the full lifecycle of robot prototypes — precision assembly, wiring and electrical integration, system bring-up, calibration, and acceptance testing — taking robots from a kit of parts to a validated, working system. You'll also serve as the first line of defense when prototypes break, triaging failures across mechanical, electrical, and software domains, performing root-cause analysis, and driving corrective actions back into designs and processes. We're looking for a resourceful engineer with real robotics experience who is as comfortable reading schematics and torquing fasteners as they are reading logs, running debuggers, and writing scripts to isolate faults. Key job responsibilities - Assemble electromechanical robotic systems and subsystems (actuators, gearboxes, sensors, vision systems, wiring harnesses, PCBAs) for early prototypes using mechanical drawings and schematics. Help create work instructions and assembly procedures. - Kit parts against the BOM, stage hardware and fixtures, set up assembly jigs and alignment rigs, and support build cycles from kit prep through final system checkout. - Build first articles from prototype kits, provide engineering with actionable build feedback (DFx), and execute rework and engineering change orders (ECOs) as designs iterate. - Fabricate, route, and terminate wiring harnesses to documented pinouts with proper strain relief, bend radius management, and cable routing for moving assemblies. - Perform pre-power-on electrical verification (continuity, pinout, insulation, polarity) and board-level integration under ESD discipline. - Bring up newly built assemblies; verify actuator, sensor, and control-loop function; perform sensor calibration; and run acceptance testing and performance characterization using standard and custom test equipment. - Use git and the Linux command line to install, configure, and run test, calibration, and diagnostic software on robot platforms; track software versions and configurations across the fleet. - Serve as the primary first responder when robot prototypes fail - systematically debug across mechanical, electrical, firmware, and software domains, reproduce faults, and perform structured root-cause analysis. - Document triage findings in clear, actionable reports; collaborate with ME, EE, controls, and software engineers to drive corrective actions into designs, firmware, and work instructions. - Track recurring failure modes and advocate for design or process changes to eliminate them. - Maintain build records, travelers, inspection checklists, and non-conformance reports (NCRs) ensuring full traceability; redline and improve work instructions with each build. - Keep the lab safe, organized, and ESD-compliant; track tooling, consumables, and inventory; follow all safety protocols for high-voltage electronics and heavy robotic assemblies. - Drive continuous improvement by developing SOPs, assembly aids, debug guides, and training materials; mentor junior technicians on assembly, test, and troubleshooting best practices. - On-site lab role; standing, bench work, and hands-on assembly for extended periods, including work in awkward positions and tight spaces. - Fine motor precision and close-vision work with small fasteners, connectors, and board-level components. - Lifting and maneuvering heavy robotic assemblies; use of required PPE. - Work around high-voltage electronics, powered actuators, and moving machinery. A day in the life Your day begins on the lab floor assembling electromechanical subsystems and integrating components — actuators, sensors, wiring harnesses, PCB assemblies — into robotic platforms following engineering drawings and work instructions. You might spend the morning building up a robotic arm assembly, carefully torquing fasteners and routing cables to spec, then transition to the test bench to run acceptance tests on completed assemblies. But the day rarely goes exactly to plan — and that's where you shine. When a prototype fails or a subsystem behaves unexpectedly, you're the first responder: pulling logs, probing signals, inspecting hardware, and systematically narrowing the fault across domains. You'll document findings, collaborate with the responsible engineer to confirm root cause, and drive the fix. Throughout the day you'll contribute to continuous improvement — refining work instructions, building debug guides, and mentoring teammates on best practices. About the team We are Frontier AI & Robotics, the team at Amazon building the next generation of embodied intelligence. We develop and implement advanced AI models that enable robots to see, reason, and act within Amazon's operations. Our lab operations team is the critical bridge between engineering vision and physical reality — the place where concepts become testable prototypes. We work closely with mechanical, electrical, controls, and software engineers who depend on our lab team's expertise to validate designs, catch problems early, and iterate quickly.

Do you want to join an innovative team applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience? Are you excited about working with large-scale datasets and developing models that solve real-world fraud and risk challenges? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to help develop scalable machine learning solutions that safeguard millions of transactions every day. In this role, you will partner with senior scientists and engineers to translate business problems into data-driven solutions, build and evaluate models, and contribute to next-generation risk prevention systems, including applications of Generative AI and LLM technologies. Key job responsibilities Apply machine learning and statistical techniques to build and improve risk management models Analyze large-scale historical data to identify risk patterns and emerging trends Develop, validate, and deploy innovative models under the guidance of senior scientists Experiment with emerging technologies, including GenAI/LLMs, to enhance automation and risk evaluation Collaborate closely with software engineers to implement models in real-time production systems Partner with operations and business teams to improve risk policies and operational efficiency Build scalable, automated pipelines for data analysis, model training, and validation Monitor model performance and provide clear reporting on key risk and business metrics Research and prototype new modeling approaches to improve system performance

Amazon Robotics is transforming warehouse automation through edge AI and machine learning applied to real-world robotics challenges. We're seeking a Applied Scientist to advance our mobile manipulation capabilities by developing learning-based approaches that enable robots to navigate and manipulate objects in dynamic fulfillment environments. This role offers the opportunity to apply state-of-the-art research to production systems operating at Amazon's unprecedented scale. Key job responsibilities - Model Development and Training: Designing and implementing the model architectures, training and fine tuning the models using various datasets, and optimize the model performance through iterative experiments - Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines. - Experimentation and Validation: Design and execute experiments to test model capabilities on the simulator and on the embodiment, validate performance across different scenarios, create a baseline and iteratively improve model performance. - Code Development: Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Research: Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Collaboration: Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart, collaborative team of enthusiastic doers that work passionately to apply innovative advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun!

Interested in setting the scientific vision for how every product at Amazon is understood, identified, and connected, with the opportunity to impact every Amazon customer and every selling partner? The Items and Relationships Platform is the foundation that establishes what every product in Amazon’s catalog is and how products relate to one another. We are rebuilding the platform to establish identity and relationships from meaning and context, using multimodal large language models and generative AI. This is a foundational problem: understanding products deeply enough to identify them, relate them, and keep the catalog trustworthy as it scales to the full breadth of Amazon’s global contribution traffic. A Principal Applied Scientist on the Items and Relationships Platform owns the science direction for product identity and product relationships. It spans how contributions are resolved into a committed product identity, how products are connected into structural relationships (variations, multipacks, newer versions, lines of products, bundles), how confident those decisions are, and how their quality is measured. You will own the strategy for multimodal understanding, representation learning, relationship inference, calibration, and evaluation, so that science capability compounds across identity and relationships rather than being built one use case at a time. This role lives at the intersection of research and production. Product identity and relationships are fundamentally an entity resolution problem: deciding what is the same product, and how different products relate, across diverse sources, languages, and formats. You will make the hard modeling calls on where sameness ends and relatedness begins, how models express and calibrate confidence, and how the platform measures quality when there is no external ground truth. Successful candidates are passionate about translating ambiguous business goals into a coherent science roadmap that enables many businesses at once. You are comfortable being the final scientific arbiter on hard cross-team decisions, and working alongside top-notch applied scientists and software engineers to turn advanced research into production systems. Key job responsibilities As a Principal Applied Scientist on the Items and Relationships Platform, you will: - Own the science direction for product identity: define how products are semantically understood, resolved, and committed as coherent identities, navigating the precision/recall and friction/selection trade-offs inherent in every decision - Build representations that decide both sameness and typed relatedness, and drive relationship inference from semantic identity rather than one pipeline per relationship type - Own structural relationship science across variations, multipacks and base products, newer versions, lines of products, and bundles, and discover new relationship types from the gap between defined policy and what identity reveals - Own calibration and evaluation science: define how models emit calibrated confidence, and build the LLM judge methodology and Catalog Identity Quality metric that measure identity and relationship quality at catalog scale - Partner with Principal Engineers on cost-aware modeling (cascades, distillation, routing) and close the loop from production signals and corrections back into training - Set science direction partnering with ACCS Applied Science teams, mentoring senior and principal-track scientists, and driving the documentation and publication of scientific innovation A day in the life On a typical day, you move between setting science direction and going deep on hard problems: reviewing a multimodal identity model or a relationship inference experiment with scientists, deciding how a borderline case (a multipack, a newer version, a near-identical variant) should be modeled, and pressure-testing whether a quality gain is real by examining the judge and its calibration. You also spend time zoomed out, shaping the longer-term science roadmap, aligning with Principal Engineers and partner teams on the identity and relationship signals they consume, and mentoring scientists as they take on the ambiguous, high-impact problems that define the platform’s future. About the team The Items and Relationships Platform advances state-of-the-art GenAI to deeply understand, uniquely identify, and meaningfully connect every product at Amazon scale. We build the canonical product identity, matching, and relationship systems that power Amazon’s universal catalog: eliminating duplicates, preventing abuse, keeping product families coherent, and integrating the world’s product data to power agentic shopping and trusted discovery for customers worldwide. We solve foundational challenges including schema-agnostic multimodal understanding, relationship inference, evaluation without ground truth, and efficient inference at reduced cost.

Application deadline: Nov 13, 2026 AWS Science of Security is dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for security, privacy, and sovereignty. The team sits within Amazon Security and operates in a fast-growing, startup-like environment within AWS. You will participate in the design, development, evaluation, and deployment of automated reasoning solutions for verifying the correctness, security, and reliability of software systems. You’ll be applying techniques such as constraint solving, model checking, static analysis, theorem proving, and program analysis to unique applications in business and engineering. How you build and deploy automated reasoning tools and verification frameworks, plus research and implement novel approaches, will add distinct value to the business and our customers. This is work on open, hard problems: the kind where the tools you build get used to secure systems that millions of customers depend on. You will dive deep alongside our world-renowned automated reasoning scientist community, bring fresh ideas from your research experience, and see your work ship rather than sit on a shelf. This is an exceptional opportunity to join a fast moving segment of AWS and help shape the technology, product and business, with notable customer and business impact. Key job responsibilities - Solve significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation - Own the design, implementation, and delivery of solutions that have a long-term quantifiable impact - Provide cross-organizational technical influence, driving effective outcomes by sharing your deep knowledge and experience - Develop fundamentally new solutions for uniquely challenging business problems A day in the life This is a unique opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a meaningful business and customer impact. About the team Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage you to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from exploring what's next.

The charter of the Entertainment Economics team includes working on high-impact projects that require economics expertise and a cross-business perspective. Our team supports Amazon’s diverse entertainment businesses – especially Prime Video, Amazon Music, Amazon MGM Studios, and Gaming (Luna) – through complex causal measurements (observational and experimental) and structural modeling. Some of our team’s areas of science expertise include marketing science, long-term value measurement, and experimentation. As a central economics team not embedded in any particular business, we strive to provide objective analyses that take a customer-obsessed, pan-Amazon perspective. This role partners with the Prime Science team to evaluate and improve the way Amazon allocates Prime subscription revenue across its benefit teams, including entertainment businesses. Prime is Amazon's premiere membership program, with over 200mm members world-wide, bringing in billions in Prime subscription revenue annually. Prime is at the center of many major company decisions that impact Amazon's customers. These decisions span a variety of industries, each reflecting the diversity of Prime benefits: fast and free shipping, entertainment & digital content, reading, healthcare, and grocery. Each of these Prime benefits is supported by a different organization, and each relies on Prime subscription revenue to make critical investment decisions. Our team partners with Prime Science to ensure that these allocations are credible, actionable, and incentive compatible for every benefit. To support this work, this role will create science models, validate them, and productionize them. In addition to stellar economics expertise, this role requires systems thinking (anticipating how complex interconnected models will respond to changes in data or modeling), strategic thinking (supporting decisions that affect 200 million+ customers and set Amazon’s direction for years to come), and scientifically novel thinking (using AI and the latest science advancements to achieve move). The ideal candidate is expert in causal inference with experience working in large, complex structural modeling environments. They are a creative problem-solver, able to identify new approaches that solve multiple problems at once while minimizing churn. They think big, not limiting themselves to what’s been done before, while tactfully accounting for the costs of major changes. They can add structure to incredibly ambiguous business contexts, making defensible assumptions and delivering measurements that drive impactful business decisions. They have effective communication and the skill to translate data-driven findings into actionable insights for senior leaders. Finally, they are empathetic and constructive. Our healthy team culture is supportive and fast-paced, and we prioritize learning, growth, and helping each other to continuously raise the bar. Impact and Career Growth In today’s entertainment landscape, critical decisions are made with data and economic models. You’ll help leaders ask the right questions, and then deliver data-driven answers, helping to create the future of Amazon. You’ll help define a long-term science vision in this space and translate it into an actionable roadmap. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding – a perfect recipe for career growth as an economist in tech. Key job responsibilities • Design and build econometric models, especially causal models, to measure the value of the business and its many features • Develop science products from concept to prototype to production, incorporating feedback from scientists and business partners • Independently identify and pursue new opportunities to leverage economic insights across entertainment and Prime • Write business and technical documents communicating business context, methods, and results to business leadership and other scientists • Serve as a technical reviewer for our team and related teams, including document and code reviews

Amazon Ads Brand Safety & Suitability protects advertisers from exposure to unsafe, unsuitable, or policy-violating content across web, mobile app, CTV, and audio advertising inventory. Our mission is to ensure that every ad impression delivered through Amazon's demand-side platform appears adjacent to content that meets advertiser trust expectations while giving brands granular controls to define suitability on their own terms. We operate at the intersection of advertiser trust, publisher quality, and supply integrity. AI is fundamentally changing the content landscape. Content is now generated at unprecedented scale, faster, cheaper, and increasingly sophisticated. Low-quality, deceptive, AI-generated, and synthetic content evolves in real time, constantly adapting to evade detection. The volume and velocity of new content entering the advertising system has outpaced traditional classification approaches. We are looking for an Applied Scientist to work on the next generation of AI-powered Brand Safety and Content Classification systems designed to protect advertisers and elevate supply quality at internet scale. This is not a traditional classification problem. You will build systems that make millisecond-level decisions across billions of content signals while continuously adapting to emerging content risks driven by generative AI. You will own the science strategy for LLM-powered classification and semantic understanding, real-time multimodal content evaluation, adversarial ML and adaptive model resilience, proactive risk intelligence and content risk hunting, AI-generated and synthetic content detection, and large-scale abusive content system identification and disruption. You will define how modern AI separates high-quality advertising inventory from unsafe, unsuitable, and policy-violating content, across web, mobile app, CTV, and audio surfaces. What Makes This Role Unique Generative AI has dramatically lowered the cost of producing deceptive, policy-evasive content, and the adversary evolves daily. Your detection systems must reason contextually, adapt rapidly, and generalize beyond previously seen content risk patterns. Static models fail here; you will build living systems that learn and respond in real time. You will do this at internet scale, developing low-latency ML and LLM-powered systems evaluating content safety, brand suitability, misinformation risk, and emerging content risk vectors across massive real-time traffic streams, making billions of decisions per day with single-digit millisecond latency constraints. This role sits at the intersection of frontier AI research and large-scale production engineering, combining deep science, system-wide impact, and business-critical outcomes. The models your team ships directly influence billions of dollars in advertising spend and the trust of the world's largest brands in Amazon DSP. The Science Problems Are Genuinely Hard You will tackle challenges including detecting sophisticated AI-generated and synthetic content, understanding nuanced contextual brand risk, identifying coordinated MFA space before they scale, balancing precision, recall, latency, explainability, and fairness, designing adaptive models resilient to adversarial evolution, and leveraging LLMs for semantic understanding in real-time, latency-constrained environments. Why This Matters Few roles offer the opportunity to work at the intersection of frontier AI, internet-scale production systems, adversarial environments, and business-critical impact, while tackling open-ended scientific challenges with real-world societal relevance. As AI reshapes the internet, the systems your team builds will define what trustworthy, high-quality digital systems look like for the next decade. Key job responsibilities - Own the science strategy for AI-powered brand safety classification. - Build LLM-powered content classification systems making billions of decisions/day at single-digit millisecond latency - Develop multimodal evaluation pipelines reasoning across text, images, audio, and video in real time - Design adaptive ML systems resilient to adversarial evolution, semantic understanding for nuanced contextual brand risk. - Define measurement frameworks and drive continuous improvement - Translate research into production, own the path from prototype to deployed model - Publish at peer-reviewed venues; contribute to the scientific community in adversarial ML, NLP, and content safety - Collaborate with software engineering teams to integrate successful experiments into large-scale, highly complex Amazon production systems.

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities Develop foundation models for content understanding using state-of-the-art deep learning and multimodal learning techniques to analyze video and text Build time sequence foundation models to understand and predict customer behavior patterns and viewing trajectories Work closely with engineers and product managers to design, implement and launch solutions end-to-end across various Prime Video experiences Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses Effectively communicate technical and non-technical ideas with teammates and stakeholders Stay up-to-date with advancements and the latest modeling techniques in foundation models, multimodal learning, and time series analysis Publish your research findings in top conferences and journals A day in the life We're using advanced approaches such as foundation models to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external conferences. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various Prime Video surfaces and devices. We work closely with the engineering teams to launch our solutions in production.

Amazon Leo is seeking a Senior Data Scientist to transform complex satellite, network, and customer terminal telemetry into actionable insights that drive system performance, reliability, and engineering decisions. In this role, you will work with large-scale datasets generated across satellite payloads, ground infrastructure, customer terminals, and end-to-end laboratory and on-orbit testing environments. You will develop analytical models, statistical methods, and automated data processing solutions to identify performance trends, detect anomalies, isolate issues, and support system validation and operational readiness. You will partner closely with systems, hardware, software, RF, and test engineering teams to establish meaningful performance metrics, correlate telemetry across multiple system components, and translate complex technical data into clear recommendations. Your work will directly influence engineering priorities, test strategies, system improvements, and launch readiness decisions. The ideal candidate combines strong statistical and data science expertise with practical experience analyzing complex engineering systems, processing high-volume time-series data, and developing scalable analytical solutions. Experience in satellite communications, wireless networks, or telecommunications is highly desirable. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities - Develop and maintain scalable data processing and analytical solutions for satellite payload, ground network, customer terminal, and laboratory test telemetry. - Analyze large volumes of structured and unstructured data to identify system performance trends, anomalies, degradation, and potential failure conditions. - Correlate telemetry across satellite payloads, RF systems, networking infrastructure, customer terminals, and application-level measurements to identify relationships and isolate root causes. - Design statistical models, machine learning techniques, and predictive analytics to detect anomalies, characterize system behavior, and identify emerging reliability or performance issues. - Establish performance baselines, key performance indicators (KPIs), and statistical methodologies to evaluate system functionality, reliability, and performance across laboratory and on-orbit environments. - Analyze automated test results, test campaign data, and regression trends to identify software and hardware performance changes, recurring failures, and gaps in test coverage. - Develop dashboards, automated reports, and visualization tools that enable engineering teams and leadership to understand system health, performance, and readiness. - Apply statistical analysis and experimental design to compare software releases, hardware configurations, and system changes, quantifying their impact on end-to-end performance. - Translate complex telemetry and analytical findings into actionable engineering recommendations that influence test priorities, troubleshooting, product improvements, and operational decisions. - Collaborate across multiple engineering organizations to define data requirements, improve telemetry quality, and develop consistent analytical methodologies. - Lead the development of reusable analytical frameworks and scalable solutions that improve engineering efficiency, reduce manual investigation, and accelerate decision-making. - Mentor engineers and data scientists in statistical methods, telemetry analysis, data interpretation, and data-driven problem-solving.