Lead Data Scientist

Be part of a pioneering initiative to build the next generation of closed-loop, causal knowledge-generating reinforcement learning systems - driven by proprietary and patented algorithmic methods.

Our advanced causal inference and reinforcement learning algorithms are already transforming operations within a Fortune 500 company, powering applications that span e-commerce content generation and targeting, as well as large-scale factory optimization and control.

In this project, you’ll help push the boundaries of adaptive learning and real-time decision-making, creating a modular, reusable codebase designed for flexible deployment across diverse, high-impact domains.

A key part of this work involves developing synthetic data generation frameworks to rigorously test and refine algorithms—ensuring they perform with the robustness and intelligence expected in real-world environments.

Requirements:

• 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.
• Excellent problem-solving, communication, and collaboration skills, with the ability to work in a fast-paced research and development environment.
• Bachelor’s, Master’s or Ph.D. in Statistics, Applied Mathematics, Machine Learning, or a related field.

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.