Data Science Manager

Requirements:

• Bachelor's or master's degree in computer science, AI/ML, electrical engineering, or a related field.
• 5-9 years of hands-on experience in computer vision with a strong track record of taking models from research/prototyping through to production deployment.
• Deep proficiency in Python and PyTorch; strong working knowledge of OpenCV, Albumentations, and image/video processing fundamentals.
• Demonstrated expertise across multiple CV tasks: object detection, instance/semantic/panoptic segmentation, anomaly detection, pose estimation, or tracking/semantic/panoptic
• Hands-on experience with modern model families: YOLO (v8 / v11 / v26), transformer-based detectors (RT-DETR, DETR, RF-DETR), segmentation models (SAM / SAM 2), and CNN backbones (ResNet, EfficientNet, ConvNeXt, Vision Transformers).
• Production experience deploying models to edge or on-prem hardware using TensorRT, ONNX Runtime, or OpenVINO; comfort with Docker, Kubernetes, and at least one cloud platform (AWS / Azure / GCP).
• Experience in a high-growth start-up or similarly fast-paced environment where scope is ambiguous, timelines are tight, and wearing multiple hats is the norm.
• Strong first-principles problem-solving ability, comfortable navigating novel, unstructured problems where no playbook exists.
• Excellent communication skills, able to distil complex technical concepts for non-technical stakeholders, write clear documentation, and present results to leadership and customers.

Good to Have:

• Prior experience leading or mentoring a small engineering team (formal management title not required; tech-lead, senior IC, or project-lead experience counts).
• Experience with industrial or manufacturing domains, understanding of factory-floor constraints, camera setups, lighting variability, and integration with PLCs/SCADA systems.
• Familiarity with zero-shot and open-vocabulary detection (Grounding DINO, YOLO-World, CLIP) and foundation models (DINOv2 SAM 3 Florence) for data-efficient learning.
• Exposure to vision-language models (GPT-4o vision, Gemini, LLaVA) for combining visual inspection with natural-language reporting or operator copilots.
• Knowledge of 3D vision, depth estimation, point-cloud processing, or multi-camera calibration for volumetric industrial inspection.
• Experience with multi-object tracking (ByteTrack, BoT-SORT) and video analytics pipelines for continuous production-line monitoring.
• Contributions to open-source CV projects, publications in top-tier venues (CVPR, ECCV, ICCV, NeurIPS), or strong Kaggle competition results.