Pradipto Das
Vice President at JPMorgan Chase & Co.
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolPradipto Das is a hands-on technical leader and VP with 13 years of experience building and commercializing AI/ML solutions for large-scale product catalog automation, now based in Palo Alto. He combines deep expertise in NLP, multimodal learning and distributed deep learning with a proven record of driving measurable business impact—saving hundreds of millions of JPY through attribute extraction, matching and classification projects. As an engineering manager at Rakuten he scaled research teams, reduced attrition, boosted compensation, and delivered production-ready frameworks and large-model fine-tuning workflows (including a 13B GPT2 fine-tune and pre-training pipelines for >100B models). He also led Rakuten’s first multi-modal catalog data challenge and authored multiple top-tier publications on product classification and generation. Colleagues describe him as equally comfortable in code, research and stakeholder roadmaps, with notable strength in turning complex academic ideas into production systems. He holds a PhD in Computer Science and a long track record of integrating research into live product pipelines.
13 years of coding experience
13 years of employment as a software developer
PhD, Computer Science, 3.678/4.0, PhD, Computer Science, 3.678/4.0 at University at Buffalo, State University of New YorkVisiting Research Fellow, Center for Soft Computing Research, Visiting Research Fellow, Center for Soft Computing Research at Indian Statistical InstituteMCA, Computer Applications, 8.84/10.0, MCA, Computer Applications, 8.84/10.0 at West Bengal University of Technology, KolkataBS, Mathematics, First Class, BS, Mathematics, First Class at Jadavpur University
Github Skills (29)
gradient10
dataflow9
portable9
gbm9
xgboost9
java9
scala9
machine-learning9
distributed-systems9
boosting9
scalable9
python8
dask8
gradient-boosting8
spark8
Programming languages (2)
Github contributions (5)
pradipto/TopicModel
Apr 2013 - Jun 2013
Contributions:8 commits in 1 month
dmlc/xgboost
Oct 2015 - Jul 2016
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Contributions:2 issues in 8 months
xgboostpythonflinkdaskdataflow
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