{"schemaVersion":"jobsearcher.job.v1","id":"b7f87f4190b7c93d8b2e0a1e","url":"https://jobsearcher.com/jobs/b7f87f4190b7c93d8b2e0a1e","canonicalUrl":"https://jobsearcher.com/jobs/b7f87f4190b7c93d8b2e0a1e","title":"Data Scientist","description":"Key ResponsibilitiesDesign, develop, and deploy computer vision models for real-world applications such as object detection, image segmentation, OCR, and video analyticsBuild scalable, low-latency inference pipelines for real-time or near real-time systemsDevelop production-quality Python code with strong engineering principles (modular design, testing, maintainability)Deploy and manage ML models using APIs and microservices architecturesWork with cloud or edge-based environments for model deployment and optimizationImplement monitoring, model evaluation, and performance tracking (e.g., drift detection, A/B testing)Collaborate with cross-functional teams including product managers, engineers, and data teamsAnalyze data quality, perform dataset annotation/augmentation, and troubleshoot model performance issuesOptimize models for performance, accuracy, and computational efficiency (GPU/latency improvements)Required Skills & ExperienceStrong hands-on experience in Computer Vision with real-world, production-deployed systemsProficiency in Python with solid software engineering fundamentals (OOP, APIs, testing frameworks)Experience with CV frameworks/tools such as YOLO, OpenCV, Detectron2, PyTorch, TensorFlowProven experience deploying ML models into production environments (not just experimentation)Experience with Docker, Kubernetes, CI/CD pipelines, and model deployment tools (e.g., MLflow)Strong understanding of APIs and microservices architecture (FastAPI, Flask, etc.)Experience working with real-time or low-latency systemsKnowledge of model monitoring, drift detection, and performance tuningStrong understanding of the data lifecycle including annotation, augmentation, and data quality challengesAbility to debug model performance issues and explain trade-offs in model design","company":"Excelon Solutions","rawCompany":"excelon solutions","city":"Grapevine","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-06-26T12:16:19.882Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Data Scientist","description":"Key ResponsibilitiesDesign, develop, and deploy computer vision models for real-world applications such as object detection, image segmentation, OCR, and video analyticsBuild scalable, low-latency inference pipelines for real-time or near real-time systemsDevelop production-quality Python code with strong engineering principles (modular design, testing, maintainability)Deploy and manage ML models using APIs and microservices architecturesWork with cloud or edge-based environments for model deployment and optimizationImplement monitoring, model evaluation, and performance tracking (e.g., drift detection, A/B testing)Collaborate with cross-functional teams including product managers, engineers, and data teamsAnalyze data quality, perform dataset annotation/augmentation, and troubleshoot model performance issuesOptimize models for performance, accuracy, and computational efficiency (GPU/latency improvements)Required Skills & ExperienceStrong hands-on experience in Computer Vision with real-world, production-deployed systemsProficiency in Python with solid software engineering fundamentals (OOP, APIs, testing frameworks)Experience with CV frameworks/tools such as YOLO, OpenCV, Detectron2, PyTorch, TensorFlowProven experience deploying ML models into production environments (not just experimentation)Experience with Docker, Kubernetes, CI/CD pipelines, and model deployment tools (e.g., MLflow)Strong understanding of APIs and microservices architecture (FastAPI, Flask, etc.)Experience working with real-time or low-latency systemsKnowledge of model monitoring, drift detection, and performance tuningStrong understanding of the data lifecycle including annotation, augmentation, and data quality challengesAbility to debug model performance issues and explain trade-offs in model design","datePosted":"2026-06-26T12:16:19.882Z","dateModified":"2026-06-26T12:16:19.882Z","hiringOrganization":{"@type":"Organization","name":"Excelon 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