Manuel Montoya Catala, Developer in Copenhagen, Denmark
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Manuel Montoya Catala

Verified Expert  in Engineering

Deep Learning Developer

Copenhagen, Denmark
Toptal Member Since
August 31, 2018

Manuel is an enthusiastic senior data scientist with 3+ years of experience in consulting and a background in machine learning research. After several years of both business and academic experience, he acquired knowledge in diverse areas ranging from statistical analysis to deep learning. He is a results-oriented person with excellent communication skills. Manuel works with dynamic teams in the standard setup consisting of Python, AWS or Azure, Agile, Git, and Visual Studio.


Amazon Web Services (AWS), Time Series Analysis, CI/CD Pipelines, PyTorch...
Danish Technical University
R, Python, SAS, Machine Learning, Data Science, Pandas, NumPy...
Danske Bank
SQL, Visual Studio, Git, Time Series Analysis, Data Analysis, Data Analytics...




Preferred Environment

Visual Studio, PyCharm, Deep Learning, Data Science, Machine Learning, Git, Amazon Web Services (AWS), Agile Practices, Python, Azure, Statistical Modeling

The most amazing...

...thing I've developed is an automatic trading platform in Python and a Bayesian deep learning architecture to solve the Q&A task in PyTorch.

Work Experience

Senior Data Scientist

2019 - PRESENT
  • Developed an end-to-end data analysis pipeline for yield optimization and predictive maintenance in a large enzyme producer company. This generated an increase in yield of 5–10%.
  • Designed and developed a churn prediction system for a big hearing aid company, resulting in the early detection of 70% of the returned devices.
  • Designed and developed a realistic patient data generation system for a healthcare company, resulting in hundreds of hours saved of manual work per clinical trial and reducing waiting time by 2–4 weeks.
Technologies: Amazon Web Services (AWS), Time Series Analysis, CI/CD Pipelines, PyTorch, Azure, Azure Data Factory, Azure Databricks, ETL, Docker, Spark, Dash, SQL, Shapely, NoSQL, Serverless, Hugging Face, MLflow, Pandas, Agile, Scrum Master, Business Proposals, Scikit-learn, Machine Learning, Data Science, Deep Learning, NumPy, Git, Jupyter, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), GPT, NetworkX, XGBoost, Data Visualization, Python, BERT, Data Analysis, Data Analytics, Amazon SageMaker, Statistical Modeling, Azure Machine Learning, Real-time Data, Convolutional Neural Networks, Artificial Intelligence (AI), Data Engineering, Machine Learning Operations (MLOps), Pytest, Jupyter Notebook, Linear Regression, Clustering, Dashboards, Tableau, Redis, Quantitative Finance, Quantitative Modeling, Quantitative Analysis, Microsoft Power BI, Predictive Modeling, Statistics, Natural Language Toolkit (NLTK)

Teaching Assistant at Multivariate Statistics

2018 - 2019
Danish Technical University
  • Created the materials for exercise sessions of the course.
  • Solved the exercises in class together with the students.
  • Developed the SAS code examples of the course and the baseline analysis of the data.
Technologies: R, Python, SAS, Machine Learning, Data Science, Pandas, NumPy, Time Series Analysis, Statistical Modeling, Linear Regression, Clustering, Statistics

Full-stack Developer

2017 - 2019
Danske Bank
  • Developed financial analysis tools for private banking clients.
  • Created dashboards for enhanced decision-making in portfolio management.
  • Developed automatic report generation systems with OpenXML.
  • Designed and implemented SQL databases to integrate the different sources of financial data.
  • Performed portfolio analysis based on allocations across currency, sectors, countries, and types of financial assets.
Technologies: SQL, Visual Studio, Git, Time Series Analysis, Data Analysis, Data Analytics, Statistical Modeling, Linear Regression, Clustering, C++, Quantitative Finance, Quantitative Modeling, Quantitative Analysis, Predictive Modeling, Statistics

Researcher and Teaching Assistant

2014 - 2016
University Madrid Carlos III of Madrid
  • Served as a teaching assistant for the master-level course on data processing.
  • Wrote a publication called "Experiments in combining boosting and deep stacked networks."
  • Developed a Python library for the deep architecture called B-ADSN.
  • Created the materials (code and presentations) for the courses Signals and Systems and Electromagnetic Fields.
Technologies: R, Pandas, Scikit-learn, NumPy, Machine Learning, Data Science, Deep Learning, Git, Agile, Time Series Analysis, Data Visualization, Python, Data Analysis, Data Analytics, Statistical Modeling, Artificial Intelligence (AI), Jupyter Notebook, Clustering, TensorFlow, Predictive Modeling, Statistics

Factory Optimization Web App for a Big Enzyme Producer

A Dash-based app for factory monitoring and optimization. I served as the senior data scientist leading a team of four people where we implemented ETL pipelines, data analytics, and dashboards. They were used for recipe and linage optimization, as well as for monitoring the factory equipment.

Trapyng | Trading in Python

Trapyng is a framework designed to build automated trading systems in Python, allowing easy development, testing, and real-world usage of trading ideas. It intends to bring proper mathematical understanding to current trading strategies used by retail traders and to apply data science in order to improve them.

The platform focuses on the proper visualization of the strategies and the understanding of the underlying methods, promoting the generation of new ideas for future trading strategies.

I started this project in 2016 with the goal of properly applying machine learning in trading. I took professional trading courses and opened a trading account with a broker to learn how retail traders think and use those insights in the system and data modeling phase.

A few manuals have been created describing the features of the system, although a large number of them are still undocumented due to lack of time. These include advanced linear models, Kalman filters, Gaussian processes, and RNNs, among others.

B-ADSNs: Research at UC3M

B-ADSN is a neural network architecture that aims to combine the expressiveness of Deep Learning, with the robustness to over-fitting of Boosting.

This project describes the B-ADSN architecture developed during my research at UC3M. The main results of this research can be found in the referenced IEEE Xplore Publication and all other materials including codes can be found in the linked repository.

The code was entirely developed from scratch in Python without TensorFlow or any other automatic differentiation library since they were not as popular at the time of development in 2015.

Directional Statistics Tool

A tool that allows the modeling of data using multi-modal time-dependent directional distributions. It can handle combinations of the Watson, Von Misses Fisher, and Gaussian distributions. It employs the expectation maximization algorithm both for independent clusters and for Hidden Markov Models of order 1.

The tool was created from scratch in Python and It contains many functionalities such as being able to combine clusters from different distributions, it can recover from degenerate clusters and it is very customizable.

The code of the final tool can be found in the referenced repository and along with its documentation. In the repository, you can also find example videos that can be generated by the tool and its applications to EEG data.

Potential Sea-Wave Energy Prediction (2014)

In this project, soft-computing techniques are used in the prediction of the sea-wave characteristic of specific areas of the Caribbean Sea. The main goal is to identify the principal predictor parameters of the wave-height of a given location.

Neural networks are used in order to predict such height, the input data is a combination of historical weather conditions and sea-waves parameters from other areas of the ocean worldwide. Meta-heuristic search techniques are used to find the subset of these parameters that produces the best prediction.

The project is completely implemented in C from scratch using the CBLAS library and POSIX threads for optimizing the speed.

The main focus of the project is the feature selection of the variables, which is done by combining a fast regression algorithm, the extreme learning machine, and discrete optimization local search heuristics such as simulated annealing and evolutionary algorithms.

Bayesian BiDAF-ELMo System in PyTorch
This Python class provides the core structure of a Bayesian BiDAF-ELMo system implemented in PyTorch for the question-answering (Q&A) task.

The architecture incorporates multiple deep learning models, including bi-directional LSTMs, highways, and attention mechanisms. Additionally, I have extended these architectures with a Bayesian approach, which can be accessed in the repository.


Python, SQL, SAS, R, C, C++


Data Science, Agile, ETL


Jupyter Notebook, Amazon Web Services (AWS), Azure, Docker


Machine Learning, Data Analysis, Data Analytics, Statistical Modeling, Artificial Intelligence (AI), Linear Regression, Predictive Modeling, Statistics, Time Series Analysis, Data Visualization, Deep Learning, Dash, Data Engineering, Clustering, Natural Language Processing (NLP), CI/CD Pipelines, Azure Data Factory, Azure Databricks, Serverless, Hugging Face, MLflow, Scrum Master, Business Proposals, DeepSNAP, PyTorch Geometric (PyG), GNN, Graphical Models, BERT, Real-time Data, Convolutional Neural Networks, Machine Learning Operations (MLOps), Dashboards, Quantitative Finance, Quantitative Modeling, Quantitative Analysis, GPT, Generative Pre-trained Transformers (GPT)


Pandas, NumPy, XGBoost, Natural Language Toolkit (NLTK), Shapely, Scikit-learn, NetworkX, PySpark, PyTorch, TensorFlow


Visual Studio, PyCharm, Jupyter, Pytest, Git, Amazon SageMaker, Azure Machine Learning, Plotly, Tableau, Microsoft Power BI




NoSQL, Redis

2016 - 2018

Master's Degree in Mathematical Modeling and Computation

Danish Technical University - Copenhagen, Denmark

2014 - 2016

Master's Degree in Data Science and Communications

University Madrid Carlos III - Madrid, Spain

2010 - 2014

Bachelor's Degree in Telecommunications' Engineering

University of Alcalá - Madrid, Spain


Machine Learning with Graphs

Stanford University

JULY 2021 - JULY 2024

AWS Certified Cloud Practitioner

Amazon Web Services