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Anil Goyal is an accomplished data scientist and researcher with a strong track record of leadership and innovation. He is currently working as Principal Data Scientist at Mastercard (AI Garage), Gurgaon, India.

In his previous roles as Senior Research Scientist at Amazon (Delhi) and Principal Data Scientist at Housing.com (Gurgaon), he lead and shaped the data science team’s direction to align with business objectives. His collaboration with cross-functional teams has led to the successful implementation of ML solutions for various problems including insight generation using Generative AI, 3D virtual tour generation, fraud detection, recommendation engines, and price prediction. Anil has spearheaded R&D initiatives, publishing research papers at top international conferences. Notably, at Housing.com, he managed a team of 10 data scientists, analysts, and QA engineers.

Prior experiences at BUDDIHealth and NEC Laboratories Europe further demonstrated his deep involvement in research and development, with a strong focus on practical implementation, securing multiple patent applications and research papers. He contributed pioneering work in medical document understanding, document classification, predictive maintenance, driver drowsiness detection, and more, highlighting their adaptability and depth of expertise.

Honored with the 40 Under 40 Data Scientist Award from Analytics Indian Magazine and 40 Under 40 High Impact Manager Award from Harappa Upgrad.

Anil’s academic journey is marked by a Ph.D. in Machine Learning from Université de Lyon, Lyon, France and Master’s in Computer Science from ENSIMAG, Grenoble INP, France. I worked under the guidance of Prof. Massih-Reza Amini at LIG, Grenoble and Dr. Emilie Morvant at LaHC, CNRS, Saint-Etienne.

You can find more information in his CV

Research Interests

Fraud Detection, Insight Generation, Generative AI, Recommendation Engine, Document Understanding, Multiview Learning, Ensemble Learning, PAC-Bayes Theory, Domain Adaptation, Transfer Learning, Lifelong Learning, Learning with Imbalanced Data, Information Retrieval.

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