Wassim Zeddini

Think-it is an engineering collective on a mission to unlock human potential through sustainable use of technology. Our high-tech, low-carbon solutions help innovative teams around the world lower costs, democratize data, and reach net-zero faster. With a focus on digital transformation, smart infrastructure, and the UN SDGs, we tackle meaningful problems that can only be solved with future-proof technology. Our team includes experts in Software Engineering, Machine Learning, and DevOps & Cloud. Founded in 2017, Think-it is home to a high-performing and diverse team of changemakers in Germany, Tunisia, and around the world — including 8+ nationalities and 40% women. We are enthusiastically committed to inclusivity and ethical technology in our collective, partner teams, and mission-aligned investor networks. To learn more, visit us at www.think-it.io or get in touch at [email protected].

Apprenticeship in fullstack JS web development, obtained a certificate after completing a four month accelerated courses in MERN stack. consisted in built an Ecommerce MERN application from scratch as a Final project using the following techs : Front-end : Bootstrap/HTML/CSS/React.js/Redux/ Javascript. Back-end : Node.js / Express.js / MongoDB.

Specification of project needs : Drafted of the technical specification of the reevalo project. Data cleaning, feature engineering and implementation of ML models for real estate price estimation. concieved reevalo’s website, Designed and built backend architecture using nodejs. Integrating the ML service and web service in a micro-services architecture. Technical Environment: Python, Machine learning, node js, react JS, docker.

graduation internship that consisted in building a noisy speech recognition system. Researched about the existing automatic speech recognition systems. Identified the limitation of existing works for speech recognition in general and noise interference in particular Data preparation and features extractions with Kaldi toolkit. worked on TIMIT dataset Implemented many neural network models as part of a pytorch-kaldi work environment for acoustic modeling such as LSTMs and CNNs. attained 87% accuracy Contributed in building a dashboard application that communicates with the ML service through API. Technical Environment: Python, Pytorch, node js, html, css, docker, bash/shell.

Predictions of the expenses in health insurance that an individual must pay based on machine learning algorithms. Contributed in building a dashboard that communicate with the ML service through an API. Development of scripts in python that creates pivot tables used a tool to estimate of the number and amount of claims to be paid in the future.