Felix B
Data Scientist / Machine Learning Specialist
Experience
Machine Learning Developer
Machine status detection in industrial 3D printing based on infrared image data
Baseline: Client has infrared cameras installed inside production machines for manual monitoring. Goal: Automate the process monitoring to identify irregularities from live images. Activities: I contributed to this project as a machine learning developer and project coordinator:
- Guided systematic data collection and pre-processing for the machine learning algorithms
- Defined the labeling process and implemented an interface to annotate the datasets
- Programmed a visual deep learning algorithm to detect machine pollution in live production
- Implemented data augmentation techniques to deal with machine heterogeneity
- Supply a containerized model with API endpoints for deployment to the production machines
- Coordinated and represented a five-person project team, prepared presentations and reports
Outcome: Provided a production-ready system to inform operators of potential faults in real time.
Technologies: PyTorch, Convolutional neural networks, docker, git, OpenCV, FastAPI, computer vision, pytorch-lightning, ipywidgets
Machine Learning Developer
Quality prediction based on time series data in a manufacturing context
Baseline: Multitude of sensors collects time series data from production machines at high frequency. Goal: Identify patterns in the data linked to final product quality. Activities: I worked in this project as a machine learning developer and project coordinator:
- Analyzed and visualized large amounts of time series data
- Explored, filtered and connected various data sources to construct consistent datasets
- Conceptualized the data pipelines and modeling approaches
- Implemented a machine learning system for quality prediction in a manufacturing context
- Tested various predictive algorithms including statistical models and deep neural networks
Outcome: Derived qualitative insights to guide process engineers in optimizing production.
Technologies: PyTorch, CNNs / ROCKET model, data wrangling, git, time series classification, explainable AI
Machine Learning Scientist
dida Machine Learning
- Conducted deep learning research projects in computer vision and time series
- Implemented commercial machine learning software in a manufacturing context
- Developed data processing pipelines for the deployment into live production
- Conceptualized application of large language models for question answering
- Represented and organized a five-person project team
- Prepared analyses and visualizations of large amounts of unstructured data
Machine Learning Scientist
Automated question answering via retrieval of internal documents
Baseline: Company employees manually search internal documentation for information. Goal: Integrate chatbot for question answering (QA) into company communication platform. Activities: I supported this project as a machine learning scientist:
- Created a demo for extractive QA based on a provided document
- Developed proof-of-concept for automatic question answering (QA) with semantic search
- Expanded processing pipeline to larger quantities of internal documents via automatic document retrieval and generative QA with large language models (LLMs)
- Informed the developers about recent advancements in natural language processing (NLP)
Outcome: Made responsive chatbot for document-based extractive QA available to all employees.
Technologies: transformers, haystack, LLMs, document retrieval, semantic search, question answering, BERT, git, beautifulsoup4
Econometrics Researcher
Estimation and analysis of variance spillover networks for academic research
Baseline: Existing network estimation methodology only works reliably on small datasets. Goal: Apply machine learning methods to analyze large datasets of economic and financial data. Activities: I realized this project as an econometrics researcher:
- Explored and compared statistical learning algorithms for multivariate forecasting
- Acquired datasets from SQL databases and set up an automated pre-processing pipeline
- Wrote object-oriented code to run cross-validated regularized machine learning algorithms
- Conducted extensive statistical and econometric analyses to empirically analyze the results
- Authored research papers that present the results at academic standard
Outcome: Published acclaimed paper in Quantitative Economics, follow-up papers under review.
Technologies: pandas / numpy, time series forecasting, scikit-learn, python, SQL, glmnet, econometrics, vector auto-regression, networkx
Teaching Assistant
Universidade Nova de Lisboa
- Supported teaching and grading of Master's courses in financial data analysis
- Generated course materials, assignments and exams
Quantitative Strategies Intern
risklab / Allianz Global Investors
- Provided data-driven research on fund performance and portfolio optimization
- Prototyped tools for investment strategies in Matlab and VBA for Excel
- Modeled time series with regularized regressions and scenario simulations
Project Management Intern
zeb Consulting
- Prepared senior management presentations to guide an IT project in risk management
Summary
Machine learning specialist with experience in commercial and academic projects. Main expertise lays in implementing AI projects in computer vision, time series, and NLP with the help of deep learning methods.
Languages
Education
Nova SBE
PhD · Econometrics · Lisbon, Portugal
Nova SBE
MSc · Finance · Lisbon, Portugal
WU Vienna
BSc · International Business Administration · Vienna, Austria
Certifications & licenses
Data Science Bootcamp
Lisbon Data Science Academy
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