Data Science Manager

The candidate will have responsibilities across the following functions:

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 across Ripik'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 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 optimisation, quantisation (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 prioritisation, 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 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.

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.