- Lead the design, development, and deployment of Reinforcement Learning algorithms and models to address complex business problems.
- Collaborate with product managers, engineers, and other stakeholders to understand requirements and define project objectives.
- Conduct thorough data analysis to identify patterns, trends, and opportunities for optimization.
- Develop and maintain scalable RL frameworks and pipelines for training, evaluation, and inference.
- Experiment with different RL techniques, architectures, and hyperparameters to improve model performance and efficiency.
- Implement state-of-the-art RL algorithms and adapt them to specific use cases, considering factors such as scalability, interpretability, and robustness.
- Work closely with software engineers to integrate RL solutions into production systems and ensure reliability and scalability.
- Stay updated on the latest advancements in Reinforcement Learning research and apply them to real-world problems.
- Mentor junior team members, provide technical guidance, and contribute to knowledge sharing within the organization.
- Bachelor's, Master's degree in Computer Science, Engineering, Mathematics, Statistics, or related field.
- 6+ years of professional experience in data science, with a focus on Reinforcement Learning.
- Solid understanding of machine learning fundamentals and deep learning techniques.
- Proficiency in programming languages such as Python, TensorFlow, PyTorch, or similar.
- Experience with RL libraries and frameworks (e.g., OpenAI Gym, Stable Baselines, RLlib).
- Strong analytical and problem-solving skills, with the ability to translate business requirements into technical solutions.
- Excellent communication skills and the ability to collaborate effectively with cross-functional teams.
- Proven track record of delivering successful data science projects from conception to deployment.
- Publications or contributions to the RL community (e.g., research papers, open-source projects) are a plus.
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