We are looking for an outstanding and passionate Data Scientist with expertise in deep learning and AI to join our team. The Ideal candidate will have an opportunity to research, design, implement, and evaluate a variety of advanced ML algorithms using the latest technology, with a specific focus on deep learning and AI applications.
Requirements:
- 5+ years of Data Scientist experience.
- Experience with large volume data storage platforms - big data.
- High-level SQL knowledge.
- Solid programming skills - Python.
- Experience with working with ML frameworks (e.g., DataBricks, Sagemaker, Vertex).
- Experience solving complex business problems using state-of-the-art Machine Learning and AI algorithms.
- Good understanding of probability and statistics.
- Understanding of data pipelines, APIs, and cloud infrastructure.
- Ability to work independently and make architectural decisions.
- Ability to mentor others, work in teams, and teach peers, and learn from them.
- Team player, ability to handle several tasks simultaneously.
- Independent, out-of-the-box thinker.
- Experience in AdTech, real-time systems, or large-scale data - an advance.
Responsibilities:
- Implement model usages from research to production, in a scalable and resilient architecture (and be part of creating and maintaining this architecture).
- Apply cutting-edge solutions while leveraging our vast amounts of data, utilizing Machine Learning and AI techniques.
- Develop online and scalable models and tools using machine learning and Reinforcement Learning techniques.
- Take on projects end-to-end from business questions all the way to production.
- Work alongside machine learning experts, big data engineers, product managers, and developers from across the organization to solve complex problems.
- Contribute to continuous learning and knowledge sharing within the data science group, to stay ahead of the curve in Machine Learning and AI advancements.
- Lead innovation initiatives and stay in tune with the latest technologies and algorithms.
Tags & focus areas
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