Zurück zur Stellenangebote
XX
Data Scientist, Computer VisionBWXT TechnologiesUnited States

Dieses Stellenangebot ist nicht mehr verfügbar

XX

Data Scientist, Computer Vision

BWXT Technologies
  • US
    United States
  • US
    United States

Über

Data Scientist, Computer Vision
BWXT Advanced Technologies is seeking a Data Scientist, Computer Vision (Classification & Deep Learning) to design, train, evaluate, and productionize image classification models that power critical decisions across our products and operations. You will own datasets, modeling, and deployment for robust, scalable visual classification—delivering measurable accuracy, reliability, and latency improvements. Location: This position is based on-site in Lynchburg, VA at the Advanced Technologies Office. Your Day to Day as a Data Scientist, Computer Vision: Lead end to end Computer Vision classification: problem definition, dataset creation, experiment design, model training, evaluation, deployment, and monitoring. Develop modern deep learning models using CNNs (ResNet/EfficientNet) and Vision Transformers (ViT/Swin), leveraging transfer learning, fine tuning, and self /weakly supervised methods as appropriate. Handle imbalanced/noisy/multi label data with class aware sampling, focal/cost sensitive losses, label smoothing, and advanced augmentations (RandAugment, MixUp, CutMix). Establish rigorous evaluation: precision/recall, F1, ROC/PR AUC, calibration, confusion analysis, per class metrics, subgroup fairness, and stress testing for lighting, occlusion, motion blur, and device variation. Build data & experiment pipelines: image ingestion, labeling QC, dataset versioning and experiment tracking with automated reproducibility. Production operations: deploy services, implement drift detection and alerting, schedule retraining, support A/B tests and human in the loop review. Cross functional collaboration with data engineering, product, operations, and quality to integrate outputs into workflows and dashboards. Required Qualifications: A bachelor's degree in computer science, electrical engineering, physics, or related field is required. A minimum of six (6) years of building and deploying computer vision classification models in production or related work experience is required. Must have strong experience with PyTorch (preferred) or TensorFlow/Keras; along with proficiency in Python (NumPy/Pandas); and familiarity with scikit learn for baselines and metrics. Must have hands on with OpenCV, torchvision/timm, albumentations; image pre /post processing and dataset curation. Must have demonstrated expertise with CNNs & Vision Transformers, transfer/self supervised learning (e.g., SimCLR/MoCo/DINO/MAE), mixed precision training, and training efficiency. Must have experience exporting and serving models (ONNX, TensorRT/OpenVINO), containerization (Docker), and CI/CD for ML services. Must be able to communicate effectively and translate model results into actionable product/operations insights. Must have a deep understanding of image classification theory and practice: loss functions, optimization, augmentation, calibration, and thresholding. Must have strong software engineering discipline: code reviews, testing, logging/observability. Must be proficient in experiment design, statistical analysis, and scientific communication. Must have a strong understanding of security/privacy best practices for visual data (PII/PHI as applicable). Must be a U.S. citizen. Must be able to obtain and maintain a U.S. Department of Energy (DOE) or Department of Defense (DOD) security clearance, whichever is required. Preferred Qualifications: MS/PhD in Computer Science, Electrical Engineering, Physics, or related field. Edge inference (NVIDIA Jetson/ARM), streaming pipelines, or multi camera systems. Data labeling operations (CVAT/Label Studio), quality control, and consensus strategies. Robustness to domain shift; techniques for generalization across environments/devices. Weak supervision, active learning, or semi-automated data curation. Interpretability (Grad CAM), calibration, and documentation (model cards, datasheets). What We Offer: Competitive salary and benefits package, including health, dental, and retirement plans. Flexible work schedules and paid time off to promote a healthy work-life balance. Professional development opportunities, including mentorship programs and sponsorship for continuing education. An inclusive atmosphere that celebrates new perspectives and supports collaboration between different generations. The chance to be part of a mission-driven organization making a positive impact on the future of energy. Opportunities for continuous learning and training to grow throughout your career! Pay: $76,000.00 - $119,000.00 The base salary range for this position in Virginia (US-VA) at the start of employment is expected to be between $76,000.00 and $119,000.00 per year. However, the base salary offered is based on local job market factors, and may vary further depending on factors specific to the selected job candidate, such as job-related knowledge, skills, experience, and other objective business considerations. Subject to these considerations, the total compensation package for this position may also include other elements, such as an annual cash incentive in addition to a full range of medical, retirement, and/or other benefits. Details of participation in these benefit plans will be provided at such time the selected job candidate receives an offer of employment. If hired, the selected job candidate will be employed 'at-will,' unless employed at a location and in a position subject to a collective bargaining agreement. The company further reserves the right to modify base salary (as well as any other discretionary payment, compensation or benefit program) at any time, including for reasons related to individual performance, company or individual department/team performance, and other market factors.
  • United States

Sprachkenntnisse

  • English
Hinweis für Nutzer

Dieses Stellenangebot wurde von einem unserer Partner veröffentlicht. Sie können das Originalangebot einsehen hier.