Job Description

Job Description
We are looking for a Machine Learning Engineer to develop predictive models that optimize the performance of advertising campaigns in a real-time bidding environment (Real-Time Bidding). The selected person will participate in the entire cycle of machine learning solutions, from research and design to implementation and evaluation, directly contributing to maximizing the return on advertising investment.
Responsibilities:
Develop machine learning models to optimize ad selection and bid calculation in real time.
Design predictive solutions to estimate user response, auction results and other challenges specific to programmatic advertising.
Research, design, develop and deploy scalable machine learning models in production environments.
Manage projects from start to finish, assuming responsibility for the development, deployment and maintenance of the implemented solutions.
Analyze large volumes of data to evaluate model performance and detect opportunities for improvement.
Design and analyze experiments using A/B tests, guaranteeing the statistical validity of the results.
Investigate advanced techniques to solve complex machine learning challenges, such as extreme data imbalance or labeling delay.
Propose new ideas and contribute to the evolution of the strategy and roadmap of artificial intelligence-based products.
Requirements:
Proactive, autonomous and problem-solving oriented profile.
Analytical skills, attention to detail and ability to manage multiple priorities efficiently.
Knowledge of statistics, including regression, probability distributions, and statistical significance testing.
Experience in Machine Learning and predictive modeling using techniques such as neural networks, logistic regression, gradient boosted decision trees and time series analysis.
Proficiency in Python and experience with libraries such as PyTorch, Pandas and NumPy.
Experience in data analysis and manipulation using SQL and Pandas.
Knowledge of version control with Git.
High level of English, both written and spoken.

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