Cientista De Dados

Generate software systems derived from various Machine Learning (ML) techniques to drive innovation.
Explore large volumes of data, create and process datasets for training and evaluating machine learning models.
Carrying out data analysis and statistical processing, data sanitisation and building features appropriate to the problem domain (feature engineering).
Explore, train, adjust and evaluate machine learning models based on classical techniques (XGBoost, genetic learning, K-means, PCA) and state-of-the-art techniques (e.g.
neural networks, generative adversarial networks, reinforcement learning, foundation models).
Master data augmentation and regularisation techniques for images, time series, tabular data and natural language processing.

Develop and deploy machine learning-based solutions in production.
Carrying out manual tests and preparing trained models for installation on different infrastructures, e.g.
cloud and embedded devices.

Requirements

Bachelor's degree.
Advanced English.
Build and train ML and AI models.
Manipulation of complex and heterogeneous data: cleaning and sanitizing, deduplication, and dataset construction.
Programming languages used in AI: Python, Scala, R (desirable) and PySpark.
AI libraries: TensorFlow, Caffe, Keras, Torch.
Use of knowledge in algorithms, statistics, regression models, decision trees, neural networks.
Experience with time series, image processing, and other complex data types.
Data Lake (Hadoop ecosystem, Spark, Kafka, etc.).
Microsoft Azure Cloud.
Integrated, automation and white-box tests.