Data Scientist with a focus on Computer Vision (Data Competency Center)

Requirements

• Established expertise in Computer Vision, reinforced by a strong proficiency in Python and a deep understanding of essential frameworks such as OpenCV, TensorFlow, and PyTorch
• Comprehensive knowledge and hands-on experience with various neural network architectures, including Convolutional Neural Networks (CNNs), Fast R-CNN, VGG, ResNet, YOLO, and SSD, for tasks such as image classification, object detection, segmentation, and real-time video processing.
• Familiarity with implementing techniques like transfer learning, data augmentation, and fine-tuning to optimize model performance
• Solid background in classical machine learning techniques, with a proven ability to integrate these methods with advanced Computer Vision technologies effectively
• Demonstrated success in managing and analyzing diverse data sources, including structured, unstructured, and real-time data streams, to prepare and optimize data for Computer Vision models
• A commitment to ongoing professional development, with a focus on staying abreast of the latest advancements in Computer Vision methodologies, tools, and classical machine learning techniques
• English proficiency: Confident Intermediate level or higher (both written and spoken)

Personal Profile

PERSONAL PROFILE:

• Demonstrated problem-solving and analytical thinking skills, with a proven track record of applying these skills to real-world challenges to identify problems, gather relevant data, and develop creative solutions
• Continuous learning mindset, ensuring you stay updated with the latest advancements in deep learning and adapt skills accordingly

Responsibilities

• Define and refine technical requirements for Computer Vision projects in close collaboration with clients and Team Leads
• Lead the creation and implementation of advanced Computer Vision models for object detection, image classification, and real-time video analysis
• Incorporate classical machine learning techniques, such as clustering, classification, and anomaly detection, into Computer Vision projects to enhance accuracy and efficiency
• Work with a variety of data sources to prepare and optimize data for Computer Vision applications, ensuring robust model training and performance
• Guide the integration of Computer Vision models into production systems, focusing on real-time performance and scalability
• Stay abreast of emerging trends and technologies in Computer Vision, experimenting with new tools and methods to push the boundaries of current models and solutions
• Communicate the value and impact of Computer Vision projects to stakeholders, effectively translating complex technical achievements into strategic business advantages
• Actively participate in the evaluation of new tools for analytical data engineering or data science