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

Step into the role ofaData Scientist atSigma Software, with afocus onComputer Vision. Inthis dynamic position within our Data Competency Center, you will spearhead innovation, craft solution architectures, and participate inpre-sales activities, leveraging the forefront ofComputer Vision technology. Project We are a team of 160+ professionals. We are very different, but afew things make usa true team: a genuine passion for our work, friendliness, and inexhaustible optimism, no matter what.  AsaMiddle Data Scientist joining BU003 Data Competency Center, you will bepivotal inshaping and driving its endeavors. RESPONSIBILITIESDefine and refine technical requirements for Computer Vision projects inclose collaboration with clients and Team Leads Lead the creation and implementation ofadvanced Computer Vision models for object detection, image classification, and real-time video analysis Incorporate classical machine learning techniques, such asclustering, classification, and anomaly detection, into Computer Vision projects toenhance accuracy and efficiency Work with avariety ofdata sources toprepare and optimize data for Computer Vision applications, ensuring robust model training and performance Guide the integration ofComputer Vision models into production systems, focusing onreal-time performance and scalability Stay abreast ofemerging trends and technologies inComputer Vision, experimenting with new tools and methods topush the boundaries ofcurrent models and solutions Communicate the value and impact ofComputer Vision projects tostakeholders, effectively translating complex technical achievements into strategic business advantages Actively participate inthe evaluation ofnew tools for analytical data engineering ordata science REQUIREMENTSEstablished expertise inComputer Vision, reinforced byastrong proficiency inPython and adeep understanding ofessential frameworks such asOpenCV, 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 asimage classification, object detection, segmentation, and real-time video processing. Familiarity with implementing techniques like transfer learning, data augmentation, and fine-tuning tooptimize model performance Solid background inclassical machine learning techniques, with aproven ability tointegrate these methods with advanced Computer Vision technologies effectively Demonstrated success inmanaging and analyzing diverse data sources, including structured, unstructured, and real-time data streams, toprepare and optimize data for Computer Vision models Acommitment toongoing professional development, with afocus onstaying abreast ofthe latest advancements inComputer Vision methodologies, tools, and classical machine learning techniques English proficiency: Confident Intermediate level or higher (both written and spoken)PERSONALPROFILE:Demonstrated problem-solving and analytical thinking skills, with aproven track record ofapplying these skills toreal-world challenges toidentify 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 PROJECTS We are a team of 160+ professionals. We are very different, but several things make us a real team: genuine passion for our work, friendliness and exhaustive optimism, no matter what. Joining the BU003 Data Competency Center as a Middle Data Scientist, you will play a decisive role in shaping and developing its activities. RESPONSIBILITIES Define and specify technical requirements for computer vision projects in close collaboration with clients and team peers Manage the creation and implementation of advanced computer vision models for object detection, image classification and real-time video analysis Implement classical machine learning methods such as clustering, classification and anomaly detection, in computer vision projects to improve accuracy and efficiency Work with disparate data sources to prepare and optimize data for computer vision applications, ensuring robust model training and performance Manage the integration of computer vision models into production systems, focusing on real-time productivity and scalability Stay abreast of emerging industry trends and technologies of computer vision, experiment with new tools and methods to push the boundaries of existing decision models Communicate the value and impact of computer vision projects to stakeholders, effectively translating complex technical achievements into strategic business benefits Actively participate in the evaluation of new tools for analytical data engineering or data science English language skills: Confident Intermediate level or higher (both written and verbal) REQUIREMENTSExperience in the field of computer vision, supported by a high level of command of the Python programming language and a deep understanding of the main frameworks such as OpenCV, TensorFlow and PyTorch Comprehensive knowledge and practical experience of working with various neural network architectures, including convolutional neural networks (CNN), Fast R-CNN, VGG, ResNet, YOLO and SSD, for tasks such as image classification, object detection, segmentation and real-time video processing Knowledge of implementation methods such as transfer learning, data augmentation and fine-tuning for model performance optimization Solid experience in classical machine learning methods with a proven ability to effectively integrate these methods with advanced computer vision technologies Having successful experience in managing and analyzing a variety of data sources, including structured, unstructured real-time data flow data, with the aim of preparing and optimizing data for computer vision models Aspiration pre-permanent professional development, with an emphasis on continuous tracking of the latest achievements in methodologies, computer vision tools and classical methods of machine learning