Simon is available for hire
Hire SimonSimon Tietze
Verified Expert in Engineering
Data Scientist and Developer
Berlin, Germany
Toptal member since November 17, 2022
Bio
Simon is a data scientist with experience in deep learning, machine learning, statistics, big data, and method development. Over his career, he has worked in various fields, including adtech, molecular biology, telecommunication networks, and hardware reliability. Simon has built predictive machine learning systems, reporting dashboards, and in-depth analytical reports, ranging from small datasets to systems operating in real time with thousands of requests per second.
Portfolio
Exago Machine Learning
R, Python 3, TensorFlow, Keras, Spark, sparklyr, Bayesian Inference & Modeling...
BEN Energy
R, Python 3, Ansible, SQL, Data Science, Machine Learning, Algorithms, MySQL...
Motorola Mobility
RStudio, R, RStudio Shiny, Python 3, Hadoop, Google BigQuery, Data Science...
Experience
- Data Science - 20 years
- Neural Networks - 20 years
- Machine Learning - 20 years
- R - 20 years
- Deep Neural Networks (DNNs) - 10 years
- Deep Learning - 10 years
- TensorFlow - 5 years
- sparklyr - 5 years
Availability
Part-time
Preferred Environment
Linux, RStudio, Python 3
The most amazing...
...project I've worked on is a mobile phone data-based population mobility analysis that provided information to several governments during the COVID-19 pandemic.
Work Experience
Principal Data Scientist | Co-founder
2018 - PRESENT
Exago Machine Learning
- Created an hourly population flow model for entire countries based on the mobile phone data used by the State of New York and the UK government to track COVID-19 measures.
- Designed and implemented a user segmentation into around 50 groups using deep learning deployed at several thousand queries per second.
- Implemented and created a model that filters unprofitable traffic in an ad auction server early in the pipeline, reducing the client's cloud cost by roughly 20%.
Technologies: R, Python 3, TensorFlow, Keras, Spark, sparklyr, Bayesian Inference & Modeling, Google Cloud, Databricks, Data Science, Machine Learning, Algorithms, PostgreSQL, Python, Neural Networks, Ggplot2, Deep Neural Networks (DNNs), Data Manipulation, Data Extraction, Large Data Sets, Data Engineering, Google Cloud Platform (GCP), Data Analytics, Data Visualization, Bash, Pandas, PyTorch, SQL-99, ETL, Docker, Amazon Web Services (AWS), Artificial Intelligence (AI), Statistical Analysis, Statistical Modeling, Predictive Modeling, Models, Version Control Systems, Communication, Modeling, A/B Testing, Data Analysis, Product Analytics, Data Pipelines, Geospatial Data, REST APIs, Computer Vision, Convolutional Neural Networks (CNNs)
Senior Data Scientist
2016 - 2018
BEN Energy
- Created customer churn models based on custom neural networks trained on censored time-to-event data. These models predicted the time until customer churn and could use partial information provided by active customers.
- Developed a SaaS predictive dashboard that provided customers with churn alerts and cross-selling recommendations.
- Presented complex modeling results to over 20 energy utility companies in interactive workshops.
Technologies: R, Python 3, Ansible, SQL, Data Science, Machine Learning, Algorithms, MySQL, PostgreSQL, Python, Neural Networks, Ggplot2, Deep Neural Networks (DNNs), Data Manipulation, Data Extraction, Large Data Sets, Data Engineering, Data Reporting, Data Analytics, Data Visualization, Bash, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Pandas, SQL-99, ETL, Docker, Amazon Web Services (AWS), Artificial Intelligence (AI), Statistical Analysis, Statistical Modeling, Predictive Modeling, Models, Version Control Systems, Communication, Modeling, A/B Testing, Data Analysis, Product Analytics, Data Pipelines, Product Development, Geospatial Data, REST APIs, Convolutional Neural Networks (CNNs)
Senior Data Scientist
2010 - 2015
Motorola Mobility
- Built a complex survival model integrating hardware properties with usage logs to investigate a newly released phone's high-return rates, which were due to the high-end model's target audience, not the hardware.
- Implemented an R library that assembled a concise device history from manufacturing, QA, sales, and the data used to inform multiple reporting and modeling tasks, including connecting sources in Oracle, Apache Hadoop, and BigQuery.
- Supported product launches with data on early product returns by building R Markdown templates that provided reports within days of a product coming to market.
Technologies: RStudio, R, RStudio Shiny, Python 3, Hadoop, Google BigQuery, Data Science, Machine Learning, Algorithms, Recommendation Systems, MySQL, PostgreSQL, Ggplot2, Data Manipulation, Data Extraction, Large Data Sets, Data Engineering, Google Cloud Platform (GCP), BigQuery, Data Reporting, Data Analytics, Data Visualization, Bash, SQL-99, ETL, Amazon Web Services (AWS), Artificial Intelligence (AI), Statistical Analysis, Statistical Modeling, Predictive Modeling, Models, Version Control Systems, Communication, Modeling, A/B Testing, Data Analysis, Product Analytics, Data Pipelines, Geospatial Data, REST APIs, Convolutional Neural Networks (CNNs)
Head of Analytics
2009 - 2010
Aloqa (acquired by Motorola Mobility)
- Developed an end-to-end big data analytics solution from the mobile client through Hadoop to the web reporting front end.
- Created a randomized keep-alive algorithm to deliver instant push messages to mobile clients before Google and Apple created APIs that enable this.
- Developed an early microservice architecture to scale from thousands to millions of users within weeks.
Technologies: R, Ruby, Java, SQL, Hadoop, Amazon Web Services (AWS), Statistical Analysis, Statistical Modeling, Predictive Modeling, Models, Version Control Systems, Communication, Modeling, A/B Testing, Data Analysis, Product Analytics, Data Pipelines, Product Development, Geospatial Data, REST APIs, Convolutional Neural Networks (CNNs)
Lead Developer
2007 - 2008
MoDeST
- Coordinated the development of a full-stack cheminformatics framework, including fingerprint, graph-based, ligand-ligand superpositioning, and protein/ligand docking methods.
- Implemented novel 3D visualizations for proteins based on OpenGL shaders, such as real-time ambient occlusion.
- Co-invented several novel techniques based on protein-ligand docking, e.g., inverting the normal process to look for molecular targets of known drugs.
Technologies: Java, Ruby, OpenGL, R, Statistical Analysis, Statistical Modeling, Predictive Modeling, Models, Version Control Systems, Communication, Modeling, Data Analysis, Product Development, Convolutional Neural Networks (CNNs)
Research Assistant
1999 - 2007
Ludwig Maximilians University of Munich
- Developed machine learning-based methods for automated diagnosis of vertigo-related diseases based on accelerometer recordings of upright stance.
- Worked on text mining, NLP, protein alignment extensions to profile the profile, and statistical approaches to validating lattice-based inference of text topics.
- Contributed to novel methods and applications in protein-ligand docking.
Technologies: MATLAB, R, Ruby, Java, Artificial Intelligence (AI), Statistical Analysis, Predictive Modeling, Models, Version Control Systems, Communication, Modeling, Data Analysis, Computer Vision, Convolutional Neural Networks (CNNs)
Experience
Population Mobility and Its Effect on the COVID-19 Pandemic in the US
Collaborated with Imperial College London on a mobility trend analysis of data grouped by user age for the entire US. We developed a pipeline combining mobile carrier and adtech location data to verify user locations. The carrier data is reliable but only precise to the cell tower level, while adtech data contains precise locations but is often fraudulent.
We used a deep learning model to augment the mobility data with user age information. The model was built and previously measured to be accurate to around 80% with five age group bins. This data was then used in a Bayesian hierarchical model analysis to attribute infection spread to different age groups in each US state.
We used a deep learning model to augment the mobility data with user age information. The model was built and previously measured to be accurate to around 80% with five age group bins. This data was then used in a Bayesian hierarchical model analysis to attribute infection spread to different age groups in each US state.
Education
2000 - 2006
Master's Degree in Computational Biology
Ludwig Maximilian University of Munich - Munich, Germany
Certifications
DECEMBER 2022 - PRESENT
Certified SAFe 5 Agile Software Engineer
Scaled Agile, Inc.
Skills
Libraries/APIs
TensorFlow, Ggplot2, PyTorch, REST APIs, Keras, Pandas, OpenGL
Tools
sparklyr, Ansible, BigQuery, MATLAB
Languages
R, Python, SQL, SQL-99, Bash, Python 3, C, Ruby, Java
Platforms
RStudio, Linux, Amazon Web Services (AWS), Databricks, Google Cloud Platform (GCP), Docker
Industry Expertise
Bioinformatics
Storage
Data Pipelines, Google Cloud, PostgreSQL, MySQL
Paradigms
ETL, Agile, Scrum, XP
Frameworks
Spark, RStudio Shiny, Hadoop
Other
Deep Learning, Data Science, Neural Networks, Machine Learning, Large Data Sets, Data Analytics, Data Visualization, Artificial Intelligence (AI), Predictive Modeling, Models, Communication, Modeling, Data Analysis, Product Analytics, Geospatial Data, Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Algorithms, Computational Biology, Data Manipulation, Data Extraction, Data Engineering, Data Reporting, Statistical Analysis, Statistical Modeling, Version Control Systems, A/B Testing, Product Development, Computer Vision, Bayesian Inference & Modeling, Google BigQuery, Recommendation Systems, Biology, Molecular Biology, Natural Language Processing (NLP), Signal Processing, Generative Pre-trained Transformers (GPT)
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