Data Scientist with Reinforcement Learning

3 weeks ago


Indianapolis, United States Katalyst Healthcares and Life Sciences Full time
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.


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