Teresa Scholz
Verified Expert in Engineering
Data Scientist and Developer
Lisbon, Portugal
Toptal member since June 15, 2020
With a Ph.D. in physics, a background in mathematics, and 13 years of experience modeling real-world data, Teresa has the skills to fulfill any data science role. Teresa enjoys working on the whole pipeline from data cleaning and analytics to a final predictive model, especially using machine learning models.
Portfolio
Experience
Availability
Preferred Environment
Microsoft Power BI, MATLAB, SQL, Git, PyCharm, Keras, Scikit-learn, Pandas, Python
The most amazing...
...thing I've developed was a model to synthesize wind data taking daily wind speed patterns into account in association with a Portuguese wind power producer.
Work Experience
Data Scientist
Delivery App Company (for Toptal)
- Developed a script that cleans the product database using Python and Regular Expressions.
- Created the ML model that predicts whether or not a product contains alcohol, based on its name.
- Worked on the development of several models that predict a product's trademark and maker based on its name and description.
- Revised a model that predicts the dimensions of a product.
Data Scientist
BNP Paribas
- Worked on a project that developed an algorithm and a platform to help human resources make hiring choices by predicting future employment and matching candidates to predicted roles.
- Kick-started and framed a project for a consultancy to match their consultants to available opportunities taking availability and skills in to consideration.
- Documented a project and identified critical points to be fixed in the next project phase.
Data Scientist
Stratio
- Contributed to the development of a method to rate diagnostic trouble codes appearing in the vehicle data which can help fleet management. This work was accepted for publication in the LOD 2020 conference.
- Researched and developed anomaly detection algorithms (supervised and unsupervised) for various applications to vehicle data using machine learning and deep learning methods.
- Prepared data, selected features, and engineered using large time-series data.
Quantitative Business Analyst
Firstwaters
- Implemented and specified the asset encumbrance report (European banking authority) in Ambit Focus and SQL.
- Specified and implemented interfaces for regulatory reporting for a newly adapted system. The reporting involved the asset classes ETD, derivatives, securities, and FX/MM.
- Performed technical and functional testing, defect management, and error analysis.
Ph.D. Candidate
University of Lisbon
- Modeled and analyzed wind turbine data and developed time-independent and time-dependent cyclic Markov models to synthesize data incorporating daily patterns of wind power production.
- Analyzed the deterministic and stochastic behavior of a wind turbine in the Langevin framework.
- Developed a parameter-free method to analyze time-series spoiled by strong, correlated measurement noise in the Langevin framework.
Research Assistant
Laboratorio Nacional de Energia e Geologia
- Implemented a data-driven segmentation of wind-power time-series using different error measures to identify ramp events.
- Implemented a partial least squares regression model for the estimation of plasmid, biomass, glucose, glycerol, and acetic acid concentration through FTIR spectra of E. coli bacteria.
- Implemented a Markov model for a weather pattern time-series resulting from the classification of wind power time-series in terms of atmospheric ciruclation patterns.
Experience
Prediction Model for the Automotive Industry
Education
Ph.D. in Physics
University of Lisbon - Lisbon, Portugal
Master of Science Degree in Medical Technology
Munich University of Technology (TUM) - Munich, Germany
Master of Science Degree in Mathematics
Munich University of Technology (TUM) - Munich, Germany
Certifications
Sequence Models
Coursera
Convolutional Neural Networks
Coursera
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
Coursera
Structuring Machine Learning Projects
Coursera
Neural Networks and Deep Learning
Coursera
Skills
Libraries/APIs
Matplotlib, NumPy, Pandas, Keras, Scikit-learn
Tools
IPython, Jira, Git, PyCharm, MATLAB, Microsoft Power BI, Jupyter, Elastic, Microsoft Excel, Tableau
Platforms
Jupyter Notebook, Linux, Alteryx
Languages
Python, SQL, Regex, Python 3
Paradigms
Anomaly Detection, Testing
Other
Data Science, Machine Learning, Statistics, Predictive Analytics, Modeling, Artificial Intelligence (AI), Data Analytics, Research, Mathematics, Data Visualization, Optimization, Neural Networks, Deep Learning, Stochastic Modeling, Natural Language Processing (NLP), Model Validation, Generative Pre-trained Transformers (GPT)
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