Nima Manafzadeh Dizbin

Nima Manaf, Ph.D.

Data Analytics Lead - Value Chain Finance Portfolio

Bridging the gap between advanced analytics and business strategy through data science, optimization, and machine learning.

nimamanaf@gmail.com
Netherlands
LinkedIn
GitHub

Professional Experience

Rabobank

Data Analytics Lead - Value Chain Finance Portfolio

August 2024 - Present

Utrecht, Netherlands

  • Leading data analytics initiatives for the Value Chain Finance Portfolio
  • Developing advanced analytical solutions for portfolio optimization and strategic decision-making
  • Driving data-driven insights to enhance value chain financing strategies

Rabobank

Data Scientist - Credit Risk

May 2022 - September 2024

Utrecht, Netherlands

  • Python implementation of an internal credit risk modelling library, and supporting modeller on its usage
  • Migrating risk modeling package to use PySpark on DataBricks
  • Authored position paper on using Machine Learning in Internal Rating Based models
  • Updated the Probability of Default Model Development procedure
  • Evaluated ML models for ECB reporting
  • Developed monitoring frameworks for Risk-Adjusted Return on Capital
  • Designed and implemented Challenger Time Series Models on Observed Default Rate Prediction for the IFRS9 team
  • Member of the EU AI-Act task force

Eindhoven University of Technology

Postdoctoral Research Associate

September 2020 - April 2022

Eindhoven, Netherlands

  • Led research in the Future Logistics Project on improving inventory management using Reinforcement Learning and AI
  • Conducted research on evolutionary training of deep neural networks
  • Applied Reinforcement Learning and Evolution Strategies for inventory management problems
  • Supervised Bachelor's and Master's student theses on various value chain and process optimization topics

Bosch Center for AI

PhD Industrial Sabbatical

February 2019 - January 2020

Stuttgart, Germany

  • Contributed to ML-driven decision-making systems for semiconductor wafer fabrication
  • Researched application of Q-Learning and Monte-Carlo Tree Search (AlphaGo algorithm) for scheduling problems
  • Developed the reinforced genetic algorithm
  • Analyzed large manufacturing network data from semiconductor wafer fabrication
  • Patent: "Learn Offline, Search Online, A Hybrid Approach to Solving Scheduling Problems" (Patent No. 389829)

Education

Ph.D. in Business Administration

Koç University

2016 - 2020

Dissertation: "Modelling and Control of Production Systems Based on Observed Inter-event Times: An Analytical and Empirical Investigation"

  • Analyzed 17 million rows of event data from semiconductor wafer fabrication
  • Developed ML and statistical methods for production time prediction
  • PhD Academic Excellence Award recipient

M.Sc. in Operations and Information Systems

Koç University

2014 - 2016

Full scholarship recipient

B.Sc. in Industrial Engineering

Sharif University of Technology

2010 - 2014

Exceptional Talent Student (top 0.1% in National University Entrance Exam)

Technical Skills

Programming & Tools

Python C++ SQL PySpark DataBricks Git Docker AWS

Data Science & ML

PyTorch Scikit-learn XGBoost LightGBM Deep Learning Reinforcement Learning Graph Neural Networks NLP

Finance & Risk

Credit Risk Modeling ALM IFRS9 Basel III Risk Analytics Monte Carlo

Operations Research

Optimization Stochastic Processes Markov Decision Processes Genetic Algorithms Scheduling Supply Chain