Daniel Renz, Data Scientist and Developer in Berlin, Germany
Daniel Renz

Data Scientist and Developer in Berlin, Germany

Member since April 7, 2022
Daniel has 10+ years of experience in scientific analysis, machine learning, and programming, with a particularly strong foundation in (Bayesian) statistics. He places emphasis on rapid prototyping but can also deploy models all the way to production. His diverse background enables him to attack new problems from multiple perspectives—he has worked as an analyst, consultant, software engineer, as well as a researcher in neuroscience, biophysics, and discrete mathematics.
Daniel is now available for hire


  • Ada Health
    Python, Google Cloud, SciPy, NumPy, Pandas, Deep Learning, Java, JavaScript...
  • Causaly
    Python, TensorFlow, SciPy, NumPy, Pandas, Deep Learning, Machine Learning



Berlin, Germany



Preferred Environment

Google Cloud, Python, Pandas, NumPy, SciPy

The most amazing...

...algorithm I've invented improved depression relapse detection by over 5% compared to the state of the art that was established just six months before.


  • Expert Data Scientist

    2020 - PRESENT
    Ada Health
    • Developed a synthetic patient case generator based on symptoms-disease networks. The project enabled new downstream analyses, such as demonstrating the effectiveness of an active learning model before deploying it to the customer.
    • Created methods for forecasting individual disease risk. Implemented risk predictors for diabetes and cardiovascular diseases.
    • Co-developed an NLP pipeline for extracting medical information from scientific articles (diseases, symptoms, risk factors, and epidemiological information) and implemented corresponding information architecture.
    • Investigated the potential use of polygenic risk scores in improving individual disease risk prediction. Planned and executed experiments for evaluating polygenic risk scores via UK Biobank data.
    • Organized bi-weekly meetings to exchange knowledge about AI and data science topics across the company.
    • Mentored other data scientists across the company.
    Technologies: Python, Google Cloud, SciPy, NumPy, Pandas, Deep Learning, Java, JavaScript, SQL, Bayesian Machine Learning, SpaCy, NLTK, Machine Learning
  • Data Scientist

    2019 - 2019
    • Aggregated and curated multiple biomedical ontologies into one coherent knowledge graph.
    • Developed a knowledge graph model and convolutional inference scheme for inference on a biomedical network graph constructed from several ontologies.
    • Applied the inference method for predicting side effects of recently approved drugs.
    Technologies: Python, TensorFlow, SciPy, NumPy, Pandas, Deep Learning, Machine Learning


  • Prediction of Relapse in Depression

    I developed a completely new method for the prediction of relapse in depression. After iterating over many different approaches, I hypothesized that the influence of individual biomarkers collected (once) today on the risk of relapse changes over time (trending upwards, downwards, or more complicated trends).

    This seemingly simple statement cannot be verified with the standard machine learning toolkit, so I developed a new method based on Gaussian processes to verify the hypothesis. Not only did this improve the current state of the start, which was just six months old at the time by more than 5%, but it also yielded various important medically directly interpretable insights into potential causes for relapse and how the influence of these causes might change over time.


  • Languages

    Python, JavaScript, SQL, Java, C++
  • Libraries/APIs

    Pandas, NumPy, SciPy, TensorFlow, PyTorch, SpaCy, NLTK
  • Other

    Bayesian Machine Learning, Probabilistic Graphical Models, Time Series Analysis, Neuroscience, Machine Learning, Deep Learning, Generalized Linear Model (GLM), Reinforcement Learning, Bayesian Inference & Modeling
  • Tools

  • Storage

    Google Cloud


  • PhD in Biomedical Engineering
    2013 - 2018
    ETH Zürich - Zürich, Switzerland

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