Ashivni Shekhawat explains how Lyft uses a mix of online learning, optimization, and control theory to operate its ride-sharing marketplace at an efficient price point. Ashivni touches on various aspects of model development, experimentation, and testing in a marketplace with strong interference, inference in scenarios with data sparsity and class imbalance, and developing scalable infrastructure for training and deploying models.
Ashivni Shekhawat is a data scientist at Lyft working on pricing. Ashivni has developed several algorithms for dynamic pricing, online learning and estimation at Lyft. Ashivni is deeply interested in statistical inference, experimentation, and machine learning. Ashivni comes from a physics and engineering background, and as conducted research and graduate studies at UC Berkeley, Cornell, and IIT Kanpur. He holds degrees in Aerospace Engineering, Physics and Applied Mechanics
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