Fernando de Meer Pardo

Data Scientist
Data Engineer
Data Scientist
Data Engineer
Data Scientist
Data Engineer
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About Me

I am a mathematician specialized in Machine Learning and Data Science. I have participated in several projects in both industry and academia involving Generative AI, Reinforcement Learning and NLP. In the past years, my PhD work has been focused on applications of NLP to Data Management problems, in particular solving Record Linkage/Entity Matching challenges via Language Models (LLMs) in real‑world settings.

  • Age 29
  • Residence Winterthur, Switzerland
  • E-mail ferdemeer@gmail.com
  • Phone +41 78 248 31 54
  • Languages Spanish (Native), English (Fluent), French (Advanced), German (Intermediate)

What I Do

Mathematics & Data Science

My background in mathematics and leadership experience in data science projects allow me to not only understand the underlying theory behind the state-of-the-art Machine Learning models, but also how to effectively apply them in practical scenarios to extract valuable insights and drive data-informed decisions.

Data Engineering

My PhD work as part of Intelligent Information Systems group of the Zurich University of Applied Sciences involved building robust and scalable data pipelines, designing efficient data storage solutions, and implementing complex data integration algorithms. This expertise allows me to effectively manage and process large datasets, ensuring data quality and accessibility for downstream analysis and applications, driving data-driven solutions for challenging real-world problems.

Scientific Writing

I have been the main author of several peer-reviewed publications (see my Google Scholar profile). I am a proficient LaTeX user and I am able to clearly and concisely communicate complex technical concepts, research findings, and methodological approaches to a specialized and general audiences.

Public Speaking

I have participated in multiple conferences & workshops. I can tailor my communication style to engage and inform different audiences, ensuring that key messages are clearly understood and resonate with listeners.

Resume

Education

Sept 2023 - May 2025
University of Zurich

Data Science PhD Program

Supervisor: Kurt Stockinger

Thesis title: "Financial Data Science, Challenges and Opportunities"

Sept 2017 - Aug 2019
Delft University of Technology

MSc Applied Mathematics

Specialization in Financial Engineering.

Sept 2013 - June 2017
Complutense University of Madrid

BSc Mathematics

Specialization in Applied Mathematics.

Experience

April 2021 – April 2025
ZHAW (Zurich University of Applied Sciences) Winterthur, Switzerland

Research Assistant

Participation in multiple research projects involving applications of Machine Learning to industry settings (parallel to the PhD work ).

Dec 2018 – April 2021
ETS Asset Management Factory Madrid, Spain

Jr Data Scientist

Worked developing applications of Generative Adversarial Networks for the development of automated quantitative investment strategies (portfolio stress testing, quantitative strategy backtesting etc.).

May 2018‑ Oct 2018
KYOS Energy Analytics Haarlem, The Netherlands

Data Science Internship

Worked modeling the European Gas Market via machine learning and alternative meteorological datasets.

Coding Skills

Python

100%%

Pandas, Numpy, Scikit-Learn etc.

100%

Pytorch/Tensorflow

100%

Hugging Face

90%

DevOps & MLOps Skills

Linux Shell

95%

Weights and Biases

95%

Data Version Control

95%

Docker

80%

SQL

80%

PySpark

80%

Agile Methodology

80%

Other Skills

  • LaTeX
  • MATLAB
  • HTML
  • CSS
  • AWS

Publications

Enriching financial datasets with generative adversarial networks

Enriching financial datasets with generative adversarial networks

Synthetic Data Generation
Mitigating overfitting on financial datasets with generative adversarial networks

Mitigating overfitting on financial datasets with generative adversarial networks

Synthetic Data Generation

Tackling the Exponential Scaling of Signature-Based Generative Adversarial Networks for High-Dimensional Financial Time-Series Generation.

Synthetic Data Generation

Gralmatch: matching groups of entities with graphs and language models

NLP

A Modular Framework for Reinforcement Learning Optimal Execution

RL

Blog

Synthetic Data Generation
Generating Financial Series with Generative Adversarial Networks
20 Mar 2019

Generating Financial Series with Generative Adversarial Networks
Synthetic Data Generation
Mitigating overfitting on Financial Datasets with Generative Adversarial Networks
16 Oct 2019

Mitigating overfitting on Financial Datasets with Generative Adversarial Networks

Forecasting
Predicting the fall: Revisiting the “Forecasting VIX peaks” experiment
01 April 2020

Predicting the fall: Revisiting the “Forecasting VIX peaks” experiment
Synthetic Data Generation
An Introduction to Time Series Signatures
23 Sept 2020

An Introduction to Time Series Signatures

Contact

Winterthur, Switzerland

+41 78 248 31 54

ferdemeer@gmail.com

Freelance/Consulting Available

How Can I Help You?

Events

Conference
EDBT 2025
25th-28th Mar 2025

28th International Conference on Extending Database Technology 2025

Projects

DataInc
DataInc
2022-2025

DataInc - Intelligent Data Integration and Cleaning