I’m Olivera, a data scientist with an interdisciplinary background. I build predictive models that help understand patterns in data and make better decisions under uncertainty.

About Me

I’m a data scientist with nine years of experience in predictive modeling and machine learning. I’ve applied these methods in fields such as public health and environmental sciences, working with data that inform real-world decisions. I’m particularly interested in creating interpretable models and visualizations that make complex analyses easier to understand and use.

I’m currently based in Germany. Most recently, I worked with PyMC Labs as a Principal Data Scientist, on developing Bayesian models to support decision-making under uncertainty. I spent the last 2 years in San Diego, CA, where I collaborated with the Data Science Alliance on a project forecasting food demand for food banks. Before that, I worked at Michael Bauer Research in Nuremberg, where I focused on predictive modeling for the labor market.

I have a PhD in Cognitive Science from Osnabrück University, where I worked on interpretable machine learning for real-world applications. Previously, I studied Medical Physics (BSc) and Electrical and Computer Engineering (MSc) at the University of Novi Sad, Serbia.

In my free time, I like hiking, making analog photos, or creating collages from old tickets, newspapers, or similar materials.

Work Experience

2025: PyMC Labs - Principal Data Scientist

  • Developed Bayesian models to support decision-making under uncertainty

  • Produced clear and effective data visualizations, enabling stakeholders to better understand model outputs and uncertainties

  • Contributed to project documentation

2024: Data Science Alliance - Data Scientist & Volunteer

  • Worked on predictive models for food demand and homelessness in San Diego County, using CDC, US Census, and HUD data.

  • Applied the Responsible Data Science framework developed by Data Science Alliance and integrated state-of-the-art research into modeling.

2019 - 2023: Michael Bauer Research GmbH - Data Scientist

  • Worked on geospatial models to predict daytime population shifts across Europe and North America, incorporating consumer behavior, market trends, and socio-economic data.

  • Contributed to developing a geospatial model for forecasting unemployment rates across Europe and North America.

  • Worked on predictions of economic indicators such as purchasing power, consumer styles, and income.

  • Defined and validated KPIs for economic forecasting.

  • Built tools and dashboards to visualize complex data and automated processes to improve efficiency.

2015 - 2019: Institute of Cognitive Science - Research Assistant

  • Researched the interpretability of machine learning models, focusing on their application in high-stakes decision-making.

  • Developed three machine learning models for different applications: predicting epileptic seizures, forecasting infectious diseases, and predicting environmental variables.

  • Won a special prize at the 'hack4health' hackathon and interned at the Robert Koch Institute in Berlin. There, I led a project to develop a Bayesian model for predicting the spread of infectious diseases.

  • Collaborated with the Juelich Research Center to develop a Bayesian hierarchical model for predicting environmental variables.

  • Taught a semester-long course on ensemble methods in machine learning and mentored bachelor and master theses.

Education

2015 - 2023: PhD studies in Cognitive Science, Osnabrück University, Germany

2013 - 2014: Master studies in Electrical and Computer Engineering, University of Novi Sad, Serbia

2009 - 2013: Bachelor studies in Medical Physics, University of Novi Sad, Serbia

Volunteering

2024 - 2025:

  • Women in Tech San Diego - Leadership Team Member

  • Women Coders Inclusive Org San Diego - Volunteer

2024: Data Science Alliance - Volunteer

Tech Stack

Areas of Expertise: Data Science, Machine Learning, Data Visualization, KPI Framework Development

Data Science Methods: Predictive Modeling, Time-series Analysis, Forecasting, Bayesian inference, Probabilistic Modeling, Generalized Linear Models (GLM), Markov Chain Monte Carlo, Causal inference, Spatio-temporal models

Programming Languages: Python, SQL, R, Alteryx

Programming Libraries: Jupyter, Pytorch, scikit-learn, SciPy, Pandas, OpenCV, NumPy, PyMC, Shapely, tidyverse

Data Visualization: matplotlib, ggplot2, Plotly, seaborn