Lead Data Scientist, EPAM

Data Scientist
United Arab Emirates, Abu Dhabi
October 28, 2026
Full Time

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Job Description

The EPAM company is looking for a lead data scientist to head the development of the latest computer vision approaches in their video analytics software. This position requires experience in machine learning, model selection, experiments, and deployment, mostly connected to object detection, object tracking, re-identification, action recognition, and image segmentation. As a lead data scientist, the applicant will have to work with computer vision approaches based on YOLO, Transformers, ViT, and RF-DETR architectures. Also, other responsibilities might involve the creation of multi-camera tracking systems, optimization of models by means of TensorRT, ONNX, and OpenVINO.

Experience: 10+ years 

Qualification: A bachelor’s or master’s degree in computer science, engineering, or a related field; a PhD is an advantage. 

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Responsibilities

  • Develop new computer vision algorithms that can detect, track, re-identify, classify actions, and perform segmentation of objects.
  • Help select and experiment with the YOLO, Transformers, ViT, RF-DETR, and Vision-Language Model architectures.
  • Deploy ML models for production via TensorRT, ONNX, and OpenVINO.
  • Design multichannel video analysis systems along with training, deployment, and monitoring of models.
  • Provide mentoring to data scientists and engineers, do code reviews, and provide technical solutions to stakeholders.

Qualifications

  • Bachelor’s or master’s in computer science/engineering or related domains; preference will be given to Ph.D. holders.
  • More than 10 years of experience in implementing machine learning models along with knowledge of computer vision.
  • Programming skills in Python are excellent, and there is practical experience in working with PyTorch and TensorFlow.
  • Practical experience with model architectures such as YOLO, Transformer, Vision Transformer, RF-DETR, and vision-language models. 
  • Optimization of models, Edge-AI limitations, MLOps video analytics pipelines 

Preferred Qualifications

  • Understanding of development experience in LLMs and TensorFlow technology.
  • Knowledge of solution architecture and the MLOps approach.
  • Familiarity with technologies of model optimization such as OpenVINO, ONNX Runtime, and TensorRT.
  • Experience in the development of object detection solutions using Python and PyTorch.
  • Skills to implement machine-learning techniques for scalable AI solutions.

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