Generative AI Tools

With a foundational passion for mathematics and a technical background in computer science, this candidate specializes in data science and operational research, aiming to generate significant value for clients. Currently pursuing a degree in Computer Engineering, their professional trajectory includes experience as a Data Scientist at LogComex, where they successfully migrated and automated legacy data pipelines, developed predictive regression models, and enhanced logistical efficiencies through advanced graph modeling. Previously at IBM, they analyzed customer behavior via graph theory, scraped web data for financial insights, and implemented real-time monitoring systems for safety improvements. Their academic pursuits are complemented by certifications in data science and educational contributions, demonstrating a commitment to excellence and continuous learning in the field.

A highly skilled professional in Electronic Engineering with a strong focus on Computer Vision and Artificial Intelligence, possessing robust qualifications through a Bachelor's and ongoing Master's degree in Electrical Engineering. Demonstrated expertise in image processing, object detection, anomaly classification, and semantic segmentation utilizing advanced frameworks such as PyTorch, TensorFlow, and OpenCV. Current research as an AI & Computer Vision Researcher involves developing software to inspect visual defects in notebooks using sophisticated techniques aligned with Agile methodology. Proficient in handling various communication protocols and embedded systems, combined with practical experience in natural language processing and thermographic imaging solutions. A commitment to innovation is evidenced by participation in international educational programs and ongoing professional development in cutting-edge technologies.

A proficient researcher and developer with significant expertise in biological molecular research and diagnostic imaging technologies, specializing in public health solutions. Developed and assembled hardware for diagnostic equipment, leading to the publication of three Python packages aimed at enhancing system functionality. Experienced in computer vision, having designed feature extractors and combiners that utilize machine learning methodologies for data analysis. Holds a Master's degree in Materials Engineering and Sciences and a Bachelor's degree in Physics from a leading institution, demonstrating a solid foundation in scientific principles and applications.

Possessing a strong foundation in Chemical Engineering and Computer Science, this professional has made significant contributions in the chemical industry as a laboratory and production intern, and as an innovation researcher focusing on Industry 4.0 technologies. Currently employed as a Data Scientist, expertise encompasses developing solutions in Data Science, Analytics, Machine Learning, and IoT. Proficient in statistical modeling, computer vision, and deep learning using Python-based frameworks and tools, with hands-on experience integrating edge devices and cloud services. Additionally, has demonstrated academic involvement through research initiatives and software development for educational purposes, further enhancing skills in advanced analytics and process control.

A highly skilled data scientist and computer engineer with a Bachelor’s degree in Computer Engineering from Universidade de Araraquara, a Master’s in Computer Science and Computational Mathematics from the Instituto de Ciências Matemáticas e de Computação at USP, and a recent MBA in Data Science from USP/Esalq. Currently a doctoral candidate, engaged in advanced research focusing on machine learning applications for data quality assessment. Proven ability to apply theoretical knowledge to practical challenges, illustrated by substantial experience at Ford, where innovative facial recognition systems were developed utilizing advanced programming skills in Python and machine learning frameworks. Demonstrates strong analytical capabilities, collaborative spirit, and effective communication skills while mentoring MBA students.

With a robust academic background and extensive post-doctoral experience at the intersection of biology and technology, this candidate currently contributes to innovative projects at Tulane University, USA, utilizing neural networks to enhance the world's largest fish image database for AI applications. Previous tenure at EMBL-EBI in the UK included the development of Python applications that revolutionized biological signal interpretation through machine learning and computer vision for analyzing cardiac rhythms and caudal movements. Experience at the National Institute for Amazonian Research in Brazil established a foundational expertise in scientific research, focusing on advanced technologies for analyzing captive animal behavior. Proficient in technologies such as Python, OpenCV, Scikit-learn, and TensorFlow, combined with a strong command of algorithms and data structures, positions this candidate to adeptly tackle complex challenges in the fields of data science and biology.

This candidate possesses a robust academic foundation in Computer Science, complemented by a Master's degree and ongoing doctoral studies in the same field. With extensive experience in Data Science projects concentrated on Machine Learning applications, they have demonstrated proficiency in a wide array of tools including pandas, numpy, scipy, matplotlib, seaborn, sklearn, pytorch, and various AutoML frameworks such as H2O and TPOT. Current roles include leading AI initiatives focused on vulnerability analysis in information systems and conducting research on imbalanced regression problems. Previous engagements encompass collaborative research on time series and machine learning for renewable energy generation forecasting, as well as academic positions teaching critical computer science subjects, reflecting a strong blend of practical and theoretical expertise.

Possessing a Bachelor's and a Master's degree in Physics from a state university, this candidate demonstrates expertise in applying mathematical tools, logical reasoning, and scientific methods to practical problem-solving across various domains that utilize modeling and data analysis. The individual showcases substantial proficiency in programming as applied to fields such as Analytics, Machine Learning, and finite element simulations, as well as in control and automation systems. With robust experience in developing and implementing AI, Computer Vision, and advanced machine learning solutions, this candidate has led multidisciplinary teams and adopted MLOps practices in the sector. Furthermore, they are well-versed in documenting projects effectively and delivering impactful oral presentations in both Portuguese and English, making them particularly suited for interdisciplinary collaborations.