Data Science Mid\-level Lisboa, Braga, Viana do Castelo The Data Science area is dedicated to data analysis and modeling, solving diverse and multidisciplinary problems with the support of knowledge from various fields (mathematics, software engineering, and machine learning algorithms). Our focus lies on implementing impactful solutions that generate value. With this in mind, great emphasis is placed on our development methodologies and problem-solving approaches. Python is the primary programming language, and whenever necessary, it is applied in a distributed manner, enabling scalable solution implementation. Main Responsibilities As a Data Scientist \& Machine Learning Engineer, you will have the opportunity to solve real-world problems across key industries (Education, Science, Banking, Telecommunications, and Retail). Your mission will involve applying various data science techniques—such as training and building predictive models, and analyzing and visualizing data—to specific challenges within these sectors, aiming to extract valuable insights through the extraction, preparation, and manipulation of large volumes of data. **Daily Responsibilities:** Implement solutions for data extraction, transformation, and standardization, turning raw data into meaningful and relevant information; Research, analyze, and implement modern and efficient algorithms to make optimal use of the datasets under analysis; Conduct exploratory data analyses and produce reports based on the data, enabling actionable conclusions; Optimize overall solutions using machine learning models and insights derived from data analysis. **Stacks:** Python, PySpark, R Requirements Academic Background Bachelor’s or Master’s degree in Computer Science, Information Systems, Mathematics, Computational Science, or related fields. Professional Experience Minimum of 1 year of experience as a Data Scientist, with solid knowledge of SQL, data manipulation, and data visualization techniques. Technical Skills Experience in Python programming, particularly with machine learning and mathematical libraries such as NumPy, SciPy, scikit\-learn, pandas, Keras, TensorFlow, PyTorch, or PySpark. Machine Learning Knowledge Experience with supervised and unsupervised machine learning techniques, as well as data mining and pattern recognition algorithms. Data Exploration Enthusiasm for exploring, preparing, and transforming data, converting raw data into useful information. Languages Proficiency in English, both spoken and written. **Bonus Points:** Experience with Microsoft Azure, GCP, and/or AWS; Knowledge of statistics and mathematical concepts, such as linear and logistic regression, data distribution, and normality tests.