EagleView

Lead Data Scientist

Job description

Lead Data Scientist

Professional Experience: 6-9 years

Location: Bangalore

Required Skills:

  • Prior experience developing deep learning models for computer vision applications. Hands on experience in areas like image classification, object detection and instance segmentation.
  • Understanding of deep learning fundamentals including network architectures, training artefacts, overfitting and regularisation in neural networks, batch normalisation and so on.
  • Proficiency in python.
  • Proficiency in numpy and opencv.
  • Proficient in at least one of the deep learning frameworks e.g. PyTorch, MxNet or Tensorflow.
  • Familiarity contributing and maintaining code in github.
  • Understanding of SQL and docker technology is a bonus.

Responsibilities:

  • Contribute across different stages of deep learning model development including data collection, data cleaning, model development, validation and deployment.
  • Develop deep learning models for a wide variety of tasks spanning object detection, segmentation, tracking, synthesise and so on.
  • Continuously improve the quality and performance of existing deep learning models by applying latest research in the field.
  • Contribute in developing models in areas like self-supervised and semi-supervised learning

Educational Background

B.Tech/M.Tech (Optional), B.Sc./M.Sc., Computer Science, Maths, Statistics or equivalent field

Lead Data Scientist

Professional Experience: 6-9 years

Location: Bangalore

Required Skills:

  • Prior experience developing deep learning models for computer vision applications. Hands on experience in areas like image classification, object detection and instance segmentation.
  • Understanding of deep learning fundamentals including network architectures, training artefacts, overfitting and regularisation in neural networks, batch normalisation and so on.
  • Proficiency in python.
  • Proficiency in numpy and opencv.
  • Proficient in at least one of the deep learning frameworks e.g. PyTorch, MxNet or Tensorflow.
  • Familiarity contributing and maintaining code in github.
  • Understanding of SQL and docker technology is a bonus.

Responsibilities:

  • Contribute across different stages of deep learning model development including data collection, data cleaning, model development, validation and deployment.
  • Develop deep learning models for a wide variety of tasks spanning object detection, segmentation, tracking, synthesise and so on.
  • Continuously improve the quality and performance of existing deep learning models by applying latest research in the field.
  • Contribute in developing models in areas like self-supervised and semi-supervised learning

Educational Background

B.Tech/M.Tech (Optional), B.Sc./M.Sc., Computer Science, Maths, Statistics or equivalent field

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