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Timo Klock
Verified Expert in Engineering
Data Science Developer
Hamburg, Germany
Toptal member since September 13, 2021
Bio
Timo is a full-stack data scientist with eight years of professional experience in data-heavy applications and a PhD in machine learning and statistics. He can work in different roles on the data lifecycle in industrial applications as a data engineer, data scientist, ML engineer, or data analyst. Timo is experienced with Python and SQL, and many modern data frameworks.
Portfolio
Legal Tech Scaleup
Data Science, Machine Learning Operations (MLOps), Python, SQL, Snowflake...
Real Estate Analytics Platform
Data Science, Python, Data Engineering, SQL, Apache Airflow...
Simula Consulting
Data Science, Data Analysis, Data Engineering, Optimization, OptaPlanner...
Experience
- Data Science - 7 years
- Python - 7 years
- Machine Learning - 6 years
- Relational Database Services (RDS) - 3 years
- Cloud - 3 years
- Docker - 3 years
- Data Engineering - 3 years
- SQL - 2 years
Preferred Environment
Python, Machine Learning, Operations Research, Applied Mathematics, Cloud, Containers, Relational Databases, SQL, Warehouses, Machine Learning Operations (MLOps)
The most amazing...
...project I have contributed to was to build up a commercial real estate analytics data platform for a small startup from scratch.
Work Experience
Data Scientist, Machine Learning Engineer
2023 - PRESENT
Legal Tech Scaleup
- Developed a data-driven lead-scoring algorithm for classifying the quality of incoming law cases.
- Conceptualized and developed extendable MLOps infrastructure that streamlines the development and maintenance of ML models using open-source software (OSS) and serverless infrastructure in GCP.
- Developed a data-driven model for scoring ongoing law cases with increasingly better data coverage.
- Informed stakeholders in biweekly meetings about the importance of setting up an MLOps infrastructure if ML is planned to be used more heavily in the company.
- Consulted on questions related to the client's general data infrastructure, such as improving data quality and data coverage.
Technologies: Data Science, Machine Learning Operations (MLOps), Python, SQL, Snowflake, Prefect, MLflow, HyperOpt, Scikit-learn, Pandas, NumPy, XGBoost, AutoML, Google Cloud Platform (GCP), Google Cloud
Data Platform Engineer
2021 - PRESENT
Real Estate Analytics Platform
- Developed a cloud-based data platform on Google Cloud Platform (GCP) that fuels a commercial real estate analytics platform covering the Norwegian market.
- Researched data sources with the product team to conceptualize and develop new features for the analytics application.
- Implement data pipelines from extraction, including web scraping, API connections, and data dump imports to transform the web application's data using dbt.
- Set up and maintained a self-hosted Airflow infrastructure to schedule data pipelines and various workflows.
- Delivered customized data sets for clients using the analytics application and made the data accessible through Streamlit applications.
- Implemented and maintained an API using Python's FastAPI framework, enabling seamless data delivery to web application developers.
Technologies: Data Science, Python, Data Engineering, SQL, Apache Airflow, Data Build Tool (dbt), Airbyte, ETL, ELT, Streamlit, FastAPI, REST, Google Cloud Platform (GCP), PostgreSQL, Elasticsearch, BigQuery, Docker Compose, Poetry, GitHub, Continuous Deployment, Continuous Integration (CI)
Data Scientist, Data Engineer
2020 - 2021
Simula Consulting
- Acted as a tech consultant on several projects related to data science, machine learning, and optimization.
- Developed the data science back end of the Norwegian COVID-19 tracking app Smittestopp for identifying contacts between potentially infected individuals based on Bluetooth and GPS data.
- Served as a data engineer and analyst for a biotech company involved in discovering new drugs for aggressive forms of bile duct cancer.
- Led a small team of developers to conceptualize and implement a large-scale solver for vehicle routing problems with several business constraints, which were defined by key company stakeholders.
Technologies: Data Science, Data Analysis, Data Engineering, Optimization, OptaPlanner, Google OR-Tools, Azure, Applied Mathematics, Python, Pandas, NumPy, Matplotlib, Dash, Plotly, GitHub, SQL, Predictive Modeling
Postdoc and PhD Student
2016 - 2021
Simula Research Laboratory
- Wrote 10+ articles contributing to the fundamental understanding of commonly used methods in data science, machine learning, and AI. They were all published in internationally renowned journals and conferences.
- Wrote articles about new metaheuristic optimization methods, such as consensus-based optimization.
- Established international collaborations with leading researchers at universities in San Diego, Munich, Oslo, Genoa, and London.
- Co-supervised the research interns and PhD students working on data science and machine learning projects.
- Organized symposia, workshops, summer schools, and conferences.
- Taught courses about machine learning methods at summer schools and workshops.
- Presented research to international technical and non-technical audiences.
- Conducted long-term research visits to data science and math departments at the Technical University of Munich, Johns Hopkins University in Baltimore, and the University of California in San Diego.
Technologies: Applied Mathematics, Python, Data Science, Machine Learning, Artificial Intelligence (AI), Optimization, Metaheuristics, Pandas, Matplotlib, NumPy, Plotly, SciPy, Scikit-learn, Scikit-image, CVXOPT, Statistics, TensorFlow, PyTorch, Keras, Dimensionality Reduction, Clustering, Regression, Classification, Predictive Modeling
Visiting Postdoctoral Scholar
2019 - 2020
University of California - San Diego
- Contributed to the fundamental understanding of generative models and deep neural networks in the Department of Mathematics at UCSD.
- Established connections between UCSD and Simula Research Laboratory in Oslo.
- Co-authored scientific papers and disseminated research results to a broader audience.
Technologies: Applied Mathematics, Python, Data Science, Machine Learning, Artificial Intelligence (AI), Optimization, Metaheuristics, Pandas, Matplotlib, NumPy, Plotly, SciPy, Scikit-learn, Statistics, TensorFlow, PyTorch, Keras, Regression, Classification
Intern and Student Trainee
2015 - 2016
OHB System AG
- Acted as a full-time intern for six months and spent the next six months as a part-time student trainee in the systems engineering department of a spacecraft manufacturer OHB System.
- Developed a mathematical model for microforce emissions from reaction wheels on in-orbit satellites.
- Performed data analysis, modeling, and visualization for a comprehensive study about forces emitted by reaction wheels of in-orbit satellites.
- Corroborated mathematical models using study data.
- Co-authored a scientific paper on managing the micro-vibration impact on satellite performances, describing the study findings and developed models.
Technologies: MATLAB, Mathematical Modeling, Data Analysis
Student Research Assistant
2011 - 2016
University of Bremen
- Developed mathematical simulations of physical processes such as heat diffusion and stress and strain simulations.
- Implemented a C++ toolbox for solving the level set equation (transport equation) with mass conservation and interface re-initialization.
- Integrated level-set methods into two-phase heat equation solver based on extended finite elements and the FEniCS software framework.
- Co-authored conference presentations and technical reports about level-set methods and solving multi-phase heat equations.
Technologies: MATLAB, ParaView, Mathematical Modeling, Partial Differential Equations, Applied Mathematics, Git, GitHub, Python
Experience
Data Analysis Back End for a National Corona Tracking Application
This project aimed at developing a mobile Corona Tracking App to limit the spread of the COVID-19 disease within Norway. It was issued by the Norwegian Ministry of Health and carried out by a consortium of IT companies. I was part of the data science back-end development team, where we conceptualized and implemented contact identification algorithms based on Bluetooth and GPS data. Due to the urgency of the situation, the development environment was extremely agile. One of the key challenges in the project was the design of an efficient relational database, which allows for quickly querying necessary contact data between individuals to cope with high computational demand in times of high infection rates. Other challenges included data security due to the sensitivity of the data and dealing with uncertainty connected to Bluetooth and GPS data.
Data Analyst for Drug Discovery Analysis
This project aimed to discover novel personalized drug combinations for the treatment of aggressive types of specific cancers. My responsibilities were to develop a data pipeline that integrates data from several pilot studies into a single database (ETL and wrangling), validate the consistency of the data among different studies, and identify the most promising drug combinations based on commonly used drug interactions models. Moreover, I shared the results with key company stakeholders, for which I developed an interactive dashboard using the Python Dash and Plotly framework.
Vehicle Routing Optimization for Logistics Scale-up
The project aimed at conceptualizing and implementing a large-scale vehicle routing solver subject to several business constraints. I led a team of three developers, responsible for translating stakeholder requirements into actionable code, coordinating the development progress, and communicating the progress and results to company stakeholders. The developed algorithm was based on the OptaPlanner software framework and metaheuristic optimization methods. It allowed solving problems with several thousands of instances within 1-2 hours of computational time on a standard laptop. We further used partitioning, multi-processing, and clustering techniques to increase the efficiency of the solver and benefit from running on larger clusters.
Education
2016 - 2020
PhD in Informatics and Applied Mathematics
University of Oslo - Oslo, Norway
2010 - 2016
Master's Degree in Computational Mathematics
University of Bremen - Bremen, Germany
Certifications
AUGUST 2021 - PRESENT
DP-900: Microsoft Azure Data Fundamentals
Microsoft
JUNE 2021 - PRESENT
Apache Spark (TM) SQL for Data Analysts
Coursera
MAY 2021 - PRESENT
Deep Learning Specialization
Coursera
MAY 2016 - PRESENT
Machine Learning
Coursera
Skills
Libraries/APIs
Scikit-learn, NumPy, SciPy, Pandas, TensorFlow, Matplotlib, Keras, PyTorch, Flask-RESTful, PyMongo, PySpark, Kepler.gl, XGBoost
Tools
MATLAB, Scikit-image, Plotly, GitHub, Git, OptaPlanner, ParaView, Pytest, Google OR-Tools, Jira, Gradle, Jekyll, Azure App Service, Spark SQL, Apache Airflow, BigQuery, Docker Compose, Prefect, AutoML
Storage
JSON, Azure Cosmos DB, NoSQL, MongoDB, Relational Databases, PostgreSQL, Elasticsearch, Google Cloud
Languages
Python, XML, YAML, SQL, R, Kotlin, HTML, CSS, Snowflake
Paradigms
Object-oriented Programming (OOP), Object-oriented Design (OOD), ETL, Test-driven Development (TDD), REST, Continuous Deployment, Continuous Integration (CI)
Platforms
Google Cloud Platform (GCP), Docker, Azure, Azure PaaS, Databricks, Airbyte
Frameworks
Flask, Bootstrap, Apache Spark, JUnit, Streamlit
Other
Data Science, Applied Mathematics, Predictive Modeling, Machine Learning, Statistics, Dimensionality Reduction, Clustering, Regression, Classification, Data Inference, Statistical Learning, Optimization, Artificial Intelligence (AI), Data Analysis, Data Engineering, Dash, Metaheuristics, CVXOPT, Mathematical Modeling, Partial Differential Equations, Data Modeling, Deep Learning, Data Analytics, Cloud, Relational Database Services (RDS), Natural Language Processing (NLP), Computer Vision, OpenStreetMap, Operations Research, Generative Pre-trained Transformers (GPT), Containers, Warehouses, Machine Learning Operations (MLOps), Data Build Tool (dbt), ELT, FastAPI, Poetry, MLflow, HyperOpt
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