Neighborhood, location and space are at the crux of many decisions in life and business. Yet, gaining insights from geographic data is challenging because spatial relationships are not evident from a simple tabular representation. Iggy makes geographic data accessible and easy to use. We enrich data on US locations with relevant features describing the people and places embedded in the natural and built environment around them. We empower data scientists and machine learning practitioners to augment their datasets by addressing three key pain points: location data sourcing and cleaning, joining between levels of geographic granularity, and capturing surrounding details based on the way people actually experience places rather than simplistic, “as-the-crow-flies” approximations.
Anirudh is a Research Engineer at Iggy building machine learning products with Iggy’s trove of geospatial data. Prior to this, he has worked in machine learning teams at startups in different domains including time-series forecasting in energytech at Blueprint Power, and novel drug discovery with AI at Ordaos Bio. He is passionate about AI research, MLOps, and building highly impactful machine learning products. Outside of work, Anirudh loves spending time with his dogs, exploring New York’s arts scene and trying his hand at any and all sports.