Years of Experience
MS. Or Ph.D. in Computer Science, data science or related fields with a focus on Computer Vision/ Image Analysis and Machine/Deep learning.
- Minimum of 2+ years of industrial experience and research experience working technical challenges associated with machine learning and, computer vision and Image processing. Deep hands-on experience building computer vision systems involving algorithm development in fields such as Document Analysis, Document Parsing, Document structure extraction, image segmentation,
- Experienced in information extraction from SCAN documents preserving the semantics of document.
- Hands-on experience of using OpenCV, OpenVX, scikit-image, NumPy, SciPy, Pandas, Matplotlib, and scikit-learn.
- Hands-on ability to create Deep Neural Network models on machine learning platforms such as Tensor Flow, Keras or Pytorch.
- Demonstrated ability to write Python code for quick demonstration of ideas as well as robust, efficient, scaled-up software systems.
- Experience cleaning and wrangling real-world messy data.
- Adapt research done in academia and industry to solve problems.
- Good research background, with publications in well renowned conferences/journals.
- Must possess leadership skills and have lead a team.
- Develop and productize computer vision algorithms.
- Build highly scalable computer vision algorithms.
- Strong problem-solving skills and capable of working with developers, engineers and product leadership to create solutions.
- Maintain full responsibility for deliverables-including definition, completion, and technical quality, as well as assuring consistency across the responsibility span.
- White-board (i.e. brainstorm, problem solve) with technical staff to develop solutions for challenging problems, work with a small team to implement/test those solutions, and then quantify their accuracy.
- Documentation and formulation of report and work progress to management and clients.
- Excellent communication, interpersonal, and analytic skills.
- Following the Agile methodology (Sprints) and daily stand-ups.