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SAMEER MAURYA

ABOUT

I am a seasoned Data Scientist with five years of experience, specializing in Computer Vision and Natural Language Processing. My technical proficiency spans Python and advanced deep-learning frameworks. I have led significant computer vision and deep learning initiatives, particularly tailored for edge computing environments. My portfolio includes deploying various deep-learning solutions for clients on AWS, Heroku, and Azure. This diverse experience has equipped me with a thorough understanding of the complete machine learning project lifecycle.

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EXPERIENCE
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  • Working as a part of the Global Technology team, for the validation of different machine learning and deep learning models

  • Performing various model validations and documenting the findings, additionally developing different benchmarking to compare the performance of the different models.

  • Managing and mitigating model risk to meet or exceed regulatory and industry standards

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  • Built features for the bot platform, including Named Entity Recognition (NER) and intent detection (SURBO).

  • Worked closely with the Product team to create different ML features in the chatbot platform (SURBO)

  • developed Optical Character Recognition (OCR) as a service for PAN, ADHAAR, and Structured Receipts so that it can be utilised by a variety of Bot products used by the organisation.

  • We developed a unified machine learning application programming interface (API) that all our Chatbot products and the rest of the organisation can use.

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  • I developed a recommendation system and established 15 unique data points to be associated with each influencer in order to assist me in selecting the most effective influencer.

  • I created a model to predict ages and genders with the help of Keras. First, I labelled the data with pre-trained weights, and then I trained the model using the data that the user provided.

  • Interacted with various company executives to showcase the data points that had been gathered during the campaigns they were running.

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  • I led the research team as we designed a graphical user interface (GUI)-based image preprocessing solution for quality control and quality assurance automation that could be deployed on edge (using NVIDIA Jetson nano) and combined with robots manufactured by Universal Robotics.

  • We were able to build an automated attendance system based on facial recognition that has a 97.8 per cent accuracy rate after just needing a single photo of the user to train it with.

  • Through the implementation of a dense captioning research paper, we were able to construct an image captioned system.

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REACH OUT TO ME

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