Data Science Manager

The core responsibilities for the job include the following:

Technical Leadership and Hands-on Delivery:

• Own the end-to-end computer vision roadmap from problem framing and data strategy through model development, edge deployment, and production monitoring.
• Personally architect and build solutions for the most complex vision challenges: novel defect types, extreme class imbalance, multi-camera fusion, low-light / high-noise factory environments, and real-time inference on constrained edge hardware.
• Stay at the cutting edge of CV research and rapidly evaluate and adopt new models and techniques: YOLO26 SAM 3 Vision Transformers (DINOv2 Swin), Grounding DINO, RF-DETR, zero-shot / open-vocabulary detection (YOLO-World, CLIP), translating papers into production value.
• Define and enforce engineering standards for the vision stack: model training pipelines, data versioning (DVC), annotation workflows (CVAT, Roboflow, Label Studio), experiment tracking (W& B, MLflow), edge export formats (TensorRT, ONNX, OpenVINO), and CI/CD for model updates.
• Drive inference optimization, quantization (INT8 / FP16 GPTQ), pruning, knowledge distillation, and batching strategies to meet latency and cost targets across NVIDIA Jetson, industrial PCs, and cloud GPU instances.

Team Building and People Growth:

• Lead, mentor, and grow a team of 6-8 computer vision engineers, set clear goals, run structured code reviews and design reviews, and create an environment of rapid learning and ownership.
• Hire and onboard strong engineers; raise the technical bar through hands-on pairing, knowledge-sharing sessions, and a culture of experimentation over perfection.
• Manage sprint planning, task prioritization, and delivery timelines; balance exploratory R& D with committed product deliverables in a fast-paced start-up cadence.
• Act as the primary technical interface between the CV team and cross-functional stakeholders (product, field engineering, operations, and leadership), translating business problems into well-scoped modelling projects and communicating results clearly.

Innovation and Problem-Solving:

• Identify and frame novel, first-of-its-kind vision problems in industrial settings where off-the-shelf approaches fall short; design creative solutions combining classical image processing, deep learning, and domain heuristics.
• Champion a data-centric AI approach and invest in annotation quality, active learning, synthetic data generation, and feedback loops from production rather than only chasing bigger models.
• Establish robust evaluation frameworks: domain-specific metrics, A/B testing against production baselines, and systematic failure-mode analysis to ensure models deliver real business impact.

Requirements:

• Bachelor's or master's degree in computer science, AI/ML, electrical engineering, or a related field.
• 4-8 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.
• 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.