S
Data Scientist - Dec-2021 to Feb-2023
• Implemented cutting-edge techniques and tools in machine learning, deep
learning, computer vision and artificial intelligence using Python to make data
analysis more efficient.
• Performed large-scale exploratory data analysis (image analysis) to identify
hidden relationships between variables in large datasets. Visualized data in a
way that allows a business to quickly draw conclusions and make decisions.
• Developed artificial intelligence-based algorithms to track animal health using
computer vision and deep learning models and collaborated with cross-
functional teams to scale these models for deployment into production
environments.
• Worked in multidisciplinary teams to combine model outputs and medical
sensor data to provide clients with updated health status over the digital health
status portal.
• Implemented new statistical, machine learning, or other mathematical
methodologies using Python libraries such as Scikit-learn, Tensorflow and
OpenCV to solve complex business challenges.
• Created advanced machine learning algorithms such as regression, simulation,
scenario analysis, modeling, decision trees, XGBoost, neural networks and,
convolutional neural networks (CNNs).
• Developed data processing pipelines in Python to automate all data processing
tasks and inculcate process optimization. This helped the organization extract
and transform data from multiple sources while maintaining data quality and
integrity.
• Developed artificial intelligence models and algorithms and implemented them
to meet the organization's needs.
U
Deep Learning Researcher - Sep-2021 to Aug-2021
• Implemented research Ideas in close coordination with Professor and research
team.
• Carried out image analysis on the CASIA 2.0 image forgery dataset.
• Developed solutions for detecting and localizing image forgery using machine
learni ng and computer vision-based classification and segmentation models.
• Worked on various pre-trained models such as AlexNet, ResNet-34, ResNet-50,
U-Net using Python, Tensorflow and Keras frameworks. Worked on a modified
U-Net based architecture that used ResNet-34 as base (encoder) model.
N
Junior Computer Vision Engineer - Apr-2018 to Mar-2019
• Utilized analytical and technical expertise to derive actionable insights from
data sets by performing exploratory data analysis with minimum support.
• Worked on several applied research projects and developed products out of
them by leveraging data science and machine learning.
• Worked on machine learning and computer vision algorithms to classify designs
of various soft home furnishing products into different categories.
