Data Scientist with Reinforcement Learning

Responsibilities:

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.

Requirements:

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.