Senior/Lead Data Scientist

Description

Join a cutting-edge initiative to develop a next-generation, closed-loop causal knowledge generating reinforcement learning system based on proprietary and patented algorithmic methods. These advanced causal inference and reinforcement learning algorithms have already been deployed across multiple domains in a Fortune 500 company, powering applications ranging from e-commerce content generation and targeting to large-scale factory optimization and control.

This project will push the frontiers of adaptive learning and real-time decision-making, creating a modular, reusable codebase that can be flexibly redeployed across various high-impact systems. As part of this effort, the team will develop synthetic data generation systems to rigorously test and optimize these algorithms, ensuring they perform as if interacting with real-world environments.

Requirements

Required:

• Master’s or Ph.D. in Statistics, Applied Mathematics, Machine Learning, or a related field.
• 7+ years of experience in designing and implementing statistical computing and reinforcement learning algorithms for real-world systems.
• Expertise in reinforcement learning (RL), including multi-armed bandits, contextual bandits.
• Strong background in statistical computing, including experimental design, fractional factorial and response surface methodology, and multi-objective optimization.
• Proficiency in programming languages for machine learning and statistical computing, particularly Python.
• Experience in synthetic data generation, including stochastic process simulations.
• Strong English (min. B2 level).
• Excellent problem-solving, communication, and collaboration skills, with the ability to work in a fast-paced research and development environment.

Preferred:

• Background in experimental design for real-time decision-making.

• Familiarity and experience with bandit-based and reinforcement learning techniques such as Thompson Sampling, LinUCB, and Monitored UCB for dynamic decision-making.

   

Job responsibilities

• Collaborate with other scientists to ideate and implement proprietary reinforcement learning models for causal model-based control and adaptive optimization.
• Develop and optimize statistical models for causal inference and real-time decision- making in dynamic environments.
• Create synthetic data generation systems that accurately simulate real-world problem spaces for training and testing.
• Develop multi-objective optimization algorithms to balance competing trade-offs in various applications.
• Enhance adaptive learning approaches that enable the system to self-tune and self-correct based on environmental feedback.
• Validate and refine learning algorithms using synthetic and real-world datasets.