About the job
Location: Poland
About Us:
RTB House is a pioneering global technology company, recognized for delivering cutting-edge marketing solutions to premier brands and agencies worldwide. Our proprietary ad-buying engine stands out as the world’s first to harness the full potential of Deep Learning algorithms, empowering advertisers to achieve exceptional outcomes and reach their objectives at every stage of the customer journey. As a Machine Learning Researcher, you will play a crucial role in advancing this innovative technology.
Established in 2012, RTB House operates in over 90 markets, maintaining a private-by-design ethos. We emphasize first-party advertising and a steadfast commitment to innovation. Our comprehensive suite of Deep Learning-powered AdTech products and solutions is designed to maximize conversions, enhance customer acquisition, foster engagement, and stimulate sustained demand for our diverse global clientele.
Your Responsibilities:
- Design and implement models, predominantly deep neural networks, aimed at predicting user behavior and preferences online.
- Analyze the latest advancements in the Machine Learning domain.
- Conduct and interpret A/B tests for new methodologies.
- Develop and assess innovative strategies for modeling critical challenges, including bidding in first-price auctions.
Desired Qualifications:
- Minimum of 4 years of practical experience in Machine Learning or Data Science.
- A strong interest and commitment to continuous professional development in the Machine Learning field.
- Solid understanding of statistics and probability.
- Proficient programming skills.
Preferred Technologies:
- Python, Java, Scala
- PyTorch, NumPy, Pandas
- Jupyter Notebooks
Additional Advantages:
- Experience within the ML industry.
- Background in designing recommendation systems.
- Hands-on knowledge of our utilized tools.
Exploratory Topics:
- Implementation and evaluation of advanced model architectures, such as convolutional networks and transformers.
- Assessing discrepancies between training and production data distributions, problem scale estimation, solution identification, analysis, implementation, and deployment.
- Experiments utilizing transfer learning techniques.
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