This study presents an observation-driven sea ice albedo parametrisation by discovering an equation using symbolic regression, an interpretable machine learning method. Leveraging satellite and reanalyses data, our discovered equation identifies high sensitivity to thin snow and the weighted temperature difference between sea ice surface and 2 m air as critical to determine sea ice albedo. Our findings contribute to improving Arctic climate projections and understanding.