Diptesh B
Head Of Applied Science, Fulfillment Core at Zalando
Berlin, Germany
Summary
👤
Senior🎓
Top SchoolDiptesh B is an AI and applied science leader based in Berlin with a 17+ year background building enterprise-scale decision systems and 9 years of hands-on experience in machine learning and supply chain optimization. Currently Head of Applied Science, Fulfillment Core at Zalando, he previously founded and scaled a centralized ML/OR team at Wayfair that delivered forecasting, discrete-event simulation, digital twin and inventory positioning across multi-continent operations. At Target he led high-impact optimization products—remodel scheduling and backroom-space optimization—that together saved tens of millions annually and dramatically reduced labor and inventory waste. He combines deep modeling (GLMnet, MILP, integer programming) with production engineering (Python, PySpark, Hadoop) to move complex algorithms into reliable production APIs. Colleagues describe him as a pragmatic inventor who pairs rigorous quantitative methods with operational intuition, able to translate supply-chain complexity into simple, deployable levers. Fluent in cross-functional leadership across Europe and North America, he thrives on solving large-scale logistics problems end-to-end.
9 years of coding experience
14 years of employment as a software developer
Bachelor of Engineering - BE, Bachelor of Engineering - BE at Visvesvaraya Technological UniversityPost Graduate Program in Business Administration (MBA), Post Graduate Program in Business Administration (MBA) at IGTC
English, Hindi, Bengali
Github Skills (21)
random-forest-classifier9
knn9
xgboost-classifier9
glmnet9
clustering8
knn-classification8
pygam7
random-forest7
optimization6
code-library5
prophet5
machine-learning5
time-series5
xgboost4
python4
Programming languages (3)
Github contributions (5)
bdiptesh/CodeLib
Sep 2021 - Feb 2024
Development of new ML library
Contributions:2 releases, 2 reviews, 17 PRs in 2 years 5 months
machine-learningclusteringgap-statisticglmnetknn
ml-lib/CodeLib
Jan 2022 - Feb 2022
Code library for common machine learning algorithms
Contributions:4 releases, 4 reviews, 7 commits in 13 days
code-librarymachine-learning-algorithmsbates-grangerclusteringfbprophet
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