We're hiring a Research Data Scientist to join us at an exciting early stage. You'll play a central role in helping us turn messy, real-world industry data into AI-driven insights and products. The role is a mix of data exploration, applied ML, and experimentation with LLMs. You'll be designing and running experiments, proving what works, and collaborating closely with engineers to turn research into production-ready systems.What you will do- Wrangle large, messy datasets from legacy systems and real-world processes into structured, usable formats- Design, run, and evaluate experiments with rigorous statistical methods to validate hypotheses and measure business impact- Build and prototype ML models for forecasting, optimization, and recommendation across domains like sales, inventory, and operations- Explore and fine-tune large language models (LLMs) for practical use cases in industry workflows- Collaborate with engineers to take promising research into production, ensuring models are robust, scalable, and trustworthy- Contribute to a culture of experimentation, documenting results, sharing learnings, and driving the science behind our product decisionsWhat we look for- You believe data is the foundation for innovation, and love analyzing and modeling it to find unexpected insights that can drive business impact- You bring a scientific mindset (Master/PhD in math/science degree), rigorous about experimentation, evidence, and statistical reasoning- 4+ years of hands-on experience in data science, ML engineering, or applied research OR a PhD in a science plus 2+ years of experience- Comfortable designing and running experiments with proper baselines, controls, and statistical evaluation- Excited to explore and apply LLMs in practical, business-focused contexts- Thrive in a dynamic, iterative environment where things move quickly and research gets put into action- No industry knowledge required, you'll learn the domain as you go, and it's a fascinating oneTechnical skillset- Must: Python, familiar with common data science/ML libraries (e.g., pandas, NumPy, scikit-learn, XGBoost, PyTorch, Hugging Face), experiment design, evaluation metrics- Nice to have: LLM fine-tuning, prompt engineering, SQL/PostgreSQL