Data Science Manager

# Data Science Manager ## Company Overview A rapidly growing AI-driven enterprise technology company focused on solving large-scale operational and decision-making challenges across industrial environments. ## Job Summary We are looking for a hands-on **Data Science Manager** to lead a team of 6–8 AI — Computer Vision engineers. This is a player-coach role: you will set technical direction, personally solve the hardest modelling problems, and grow a high-performing engineering team — all within the fast-moving environment of a venture-backed industrial AI start-up. Prior formal management experience is not required; what matters is deep technical expertise, the ability to influence through craft, and the drive to build and ship in ambiguous, high-stakes settings. ## Responsibilities - **Technical Leadership & 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 across the company’s industrial portfolio (steel, cement, pharma, paints, and beyond). - 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 computer vision 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 & 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 & 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 — 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. ## Qualifications - Bachelor’s or Master’s degree (or PhD) 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 computer vision 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 distill complex technical concepts for non-technical stakeholders, write clear documentation, and present results to leadership and customers. ## Preferred Skills - 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 computer vision projects, publications in top-tier venues (CVPR, ECCV, ICCV, NeurIPS), or strong Kaggle competition results. ## Experience 4–8 years of hands-on experience in computer vision, demonstrating a strong track record of transitioning models from research to production. ## Environment This role is situated in a fast-paced start-up environment where adaptability and multitasking are keys to success. ## Salary Not specified. ## Growth Opportunities An opportunity to shape the technical direction and team culture of the computer vision function within a high-growth, venture-backed AI start-up. ## Benefits Not specified.