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; Contribute to a culture of experimentation. What we look for: Scientific mindset (Master/PhD in math/science), rigorous about experimentation and statistical reasoning; 4+ years of hands-on experience in data science/ML/applied research or a PhD plus 2+ years; Comfortable designing and running experiments with proper baselines, controls, and statistical evaluation; Excited to explore and apply LLMs; Thrive in a dynamic, iterative environment; No industry knowledge required. Technical skillset — Must: Python; 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.