Data Scientist

2 weeks ago

Thiruvananthapuram, India Waltcorp Full time
Key Responsibilities
  • Design, develop, and deploy deep learning models for image classification, object detection, segmentation, pose estimation, OCR, and related tasks.
  • Work with large-scale datasets (images, videos, annotations), including data cleaning, augmentation, and preprocessing pipelines.
  • Evaluate and fine-tune models using metrics like IoU, mAP, F1 score and accuracy.
  • Conduct research and experimentation with state-of-the-art architectures such as CNNs, Transformers (ViT, DETR), GANs, and self-supervised learning.
  • Collaborate with cross-functional teams to integrate models into production pipelines (cloud/on-prem).
  • Stay current with the latest advancements in computer vision and contribute to the company’s innovation roadmap.
  • Develop tools for model explainability and performance monitoring in production environments.


Required Qualifications
  • B.Tech, Master’s or Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
  • 3+ years of experience in developing and deploying deep learning models for computer vision tasks.
  • Strong proficiency in Python and deep learning frameworks like PyTorch and TensorFlow.
  • Hands-on experience with libraries such as OpenCV, Albumentations, MMDetection, Detectron2, or YOLOv5/8.
  • Experience training and optimizing models on GPU clusters using distributed training (e.g., PyTorch Lightning, DDP).
  • Familiarity with model deployment (ONNX, TensorRT, TorchScript) and serving (FastAPI, Flask, Triton Inference Server).
  • Experience with annotation tools (e.g., CVAT, Labelbox) and data versioning tools (e.g., DVC, Weights & Biases).
  • Strong understanding of computer vision metrics and evaluation protocols.
  • Strong skillset in mathematical algorithmics and explainability of deep learning models and frameworks


Preferred Skills
  • Knowledge of 3D vision, SLAM, multi-view geometry and YOLO.
  • Experience working with video datasets and spatio-temporal models.
  • Background in self-supervised or semi-supervised learning.
  • Familiarity with MLOps pipelines and tools like MLflow, Kubeflow, or SageMaker.
  • Experience in a domain-specific application like medical imaging, aerial imagery, or autonomous vehicles.
What We Offer
  • Fixed monthly salary and performance based incentive
  • Opportunities for professional growth and certifications.
  • A collaborative work environment that values innovation and security excellence.

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