Introduction
Hi, I am a data practitioner with experience in various data-related areas (data engineering, data analysis, data science, machine learning) and business domains (customer support, customer success, product, marketing, personalization, and sales).
I am an independent Python user, who is also proficient in multiple databases querying languages/dialects (PostgreSQL, MySQL, Snowflake, Standard SQL in BigQuery) with experience in areas ranging from data collection through its wrangling, modeling, description, and visualisation to the machine learning techniques and models (XGBoost, KMs, Word2vec).
Lately, I have gained experience with data science techniques (like segmentation or recommendation engines) and LLMs in the industry context and data engineering areas (ETL, data models, data architecture).
Data Engineer
Make, Prague, Czech Republic, 12/2022-present
- Data engineering. Development and implementation of
the CD/CI pipeline for Keboola using kb-cli, GitHub, and GitHub Actions.
Building ELT pipelines and data modelling. Building custom Extract/Load
components for various Sales/CRM platforms (Celonis, ZoomInfo) using the
request library. Addressing data quality. Code reviewing. DataOps and
FinOps. Airflow, Amazon Managed Workflows for Apache Airflow. Data
Quality using Soda.
- Machine Learning. Deploying data science projects
(LTV or personas) into production. MLOps. Strategy for the ML
infrastructure development. Code reviewing.
- Generally used tools. Python 3.11+, Snowflake, Airflow, AWS, Github,
Make, Confluence, Slack, Jira
Machine Learning Engineer
Ataccama, Prague, Czech Republic, 07/2022-10/2022
- As a part of the Data Stories team, I contributed to the back-end
(idiomatic and pydantic-driven Python, FAST API) and front-end
(TypeScript, Vue.js) codebases. The use-cases included user-defined
filters or charts and attributes recommendation.
- Unit testing via pytest (including contribution to the CI/CD
pipeline). Git using Gitlab. Linting using black, isort, and flake8.
Docker, Kubernetes, kustomize and Helm.
- Data analytics and data science - prototyping via Jupyter
Notebooks.
- Generally used tools. Python 3.10, Snowflake, MySQL, REST API, Jira,
Notion.
Data & Machine Learning Engineer
CloudTalk, Prague, Czech Republic, 04/2022-06/2022
- Data engineering. Development and implementation of
the CD/CI pipeline for Keboola using kb-cli, GitHub, and GitHub Actions.
Building ELT pipelines and data modelling. Building custom Extract/Load
components for various Sales/CRM platforms (HubSpot, Crunchbase, Apollo)
using the request library. Addressing data quality topics like entity
profiling. Code reviewing.
- Machine Learning. Developing anti-fraud prediction
models. Defined business logic with stakeholders and setting up
alerting. Implementing cookiecutter framework for developing data
science projects. Prepared a strategy for the ML infrastructure
development. Code reviewing.
- Data analytics. Ad hoc reports using Jupyter
Notebooks and Redash.
- Generally used tools. Python 3, Snowflake, ML Flow,
MySQL, REST API, Jira, Outline.
Interim Product Owner of Personalization
Rohlik Group, Prague, Czech Republic, 09/2021-03/2022
- Distributed leadership of the 4-member team (a
back-end developer, a tester, a data analyst, a machine-learning
engineer). Setting a roadmap, agile ceremonies (planning, grooming,
retrospectives, etc.).
- Feature Lifecycle Management - collecting and
prioritising business requirements based on their alignment with the
company’s business goals. Alignment with other relevant stakeholders
across the company (including preemptive identification of dependencies,
synergies, and blockers). Development and deployment of personalised
features to production, including evaluating the business impact.
- Setting up objectives, key
results, and key performance indicators of the Personalisation
squad based on the company’s OKRs (GTMHub). Reporting to the C-level
management and the board of directors.
Machine Learning Engineer
Rohlik Group, Prague, Czech Republic, 02/2021-03/2022
- Covered domains: stakeholder management in the
agile set-up (using Jira), data engineering, ML model development and
deployment in production using Keboola and AWS (both batch and real-time
predictions) and performance evaluation. Focus on the personalization of
the product and CRM, solving various classification and regression tasks
in the international context.
- Data engineering. Data models and ETL self-service
in Keboola (including REST APIs via Postman) and AWS (S3, Redshift,
MySQL).
- MLOps using AWS - Sagemaker, Lambda, CloudWatch,
Glue, API Gateway.
- A/B testing - design, execution, and evaluation.
Dashboards in Tableau, ad hoc reports using Jupyter Notebooks. Git using
GitLab and AWS Codecommit.
- Other tools - Python (including various packages like pandas or
seaborn), Snowflake, PySpark, TensorFlow.
Data Analyst
Meiro, Brno, Czech Republic, 08/2020–11/2020
- Covered domains: Data Engineering, Data Analysis, Data
Science.
- ETL pipelines management, Design of data
models.
- Integrating PostgreSQL and MySQL
with R and Python, data wrangling,
description, and visualization to advanced statistical techniques and
data science techniques (e.g. recommendation engine and
customer segmentation) in Python using
VS Code. Fundamentals of Spark.
- Linux desktop (Ubuntu), Bash, Git. Fundamentals of
Docker and CD/CI.
Data Analyst
Kiwi.com, Brno, Czech Republic, 04/2019–07/2020
- Covered domains: Product, Customer Experience, Customer
Support.
- Reports. Integrating PostgreSQL, Snowflake, and
BigQuery with R and
Python, data wrangling, description, and visualization
to advanced statistical techniques (regression models, structural
equation modelling) in R. Reporting in Markdown (R notebooks,
Jupyter Notebooks, oral presentations).
- Dashboards. I’m proficient in working with tools like Looker,
GoodData, Google Data Studio, or Retool (Plotly).
- Data engineering. Sense of data models and ETL self-service
(Keboola, dbt).
- Linux desktop (Ubuntu), Bash,
Git.
- Jira, Scrum.
Lecturer
Masaryk University, Brno, Czech Republic,
09/2015–09/2020
- Demonstrations of analyses and their interpretations, providing oral
and written feedback in the following courses:
- Statistical Data
Analysis I.
- Correlation, contingency tables, t-test, an introduction to
non-parametric tests (e.g. chi-square).
- Statistical Data
Analysis II.
- Multiple linear regression, ANOVA, logistic regression, factor
analysis, mixed effect models.
- An
Introduction to R
- An introduction to the R language, data
cleaning, wrangling, description and visualization, multivariate
analyses in the R language.
Researcher
Transport Research Centre, Brno, Czech Republic,
06/2015–03/2019
- Travel behaviour analyses, coordination of research activities,
communication with the lay public as well as the community
of experts.
- Investigator in the project Česko v pohybu. Development of
the research tool, development and implementation of the probabilistic
sampling procedure and algorithms for automated control of the data
quality.
- Methodological and analytical supervision of the research project
for the Czech Ministry of Transport related to the Public
opinion on autonomous vehicles.
- Project management of small project teams, e.g. program section for
the conference Dopravní
chování v datech.
Internships
Danmarks Tekniske Universitet, Management Engineering, Lyngby,
Denmark. 08-09/2017
Corpus Christi College, University of Cambridge, Cambridge, UK.
07-09/2015
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# Introduction

Hi, I am a data practitioner with experience in various data-related areas (data engineering, data analysis, data science, machine learning) and business domains (customer support, customer success, product, marketing, personalization, sales). 

I am an independent **Python** user, who is also proficient in multiple database querying languages/dialects (**PostgreSQL, MySQL, Snowflake, Standard SQL** in **BigQuery**) with experience in areas ranging from the data collection through its **wrangling, modelling, description, vizualisation** to the machine learning techniques and models (**XGBoost, KMs, Word2vec**). 

Lately, I have gained experience with data science techniques (like segmentation or recommendation engines) and LLMs in the industry context and data engineering areas (ETL, data models, data architecture).

## Data Engineer

Make, Prague, Czech Republic, *12/2022-present*

* **Data engineering**. Development and implementation of the CD/CI pipeline for Keboola using kb-cli, GitHub, and GitHub Actions. Building ELT pipelines and data modelling. Building custom Extract/Load components for various Sales/CRM platforms (Celonis, ZoomInfo) using the request library. Addressing data quality. Code reviewing. DataOps and FinOps. Airflow, Amazon Managed Workflows for Apache Airflow. Data Quality using Soda. 
* **Machine Learning**. Deploying data science projects (LTV or personas) into production. MLOps. Strategy for the ML infrastructure development. Code reviewing.
* Generally used tools. Python 3.11+, Snowflake, Airflow, AWS, Github, Make, Confluence, Slack, Jira

## Machine Learning Engineer

Ataccama, Prague, Czech Republic, *07/2022-10/2022*

* As a part of the Data Stories team, I contributed to the back-end (idiomatic and pydantic-driven Python, FAST API) and front-end (TypeScript, Vue.js) codebases. The use-cases included user-defined filters or charts and attributes recommendation.
* Unit testing via pytest (including contribution to the CI/CD pipeline). Git using Gitlab. Linting using black, isort, and flake8. Docker, Kubernetes, kustomize and Helm.
* Data analytics and data science - prototyping via Jupyter Notebooks.
* Generally used tools. Python 3.10, Snowflake, MySQL, REST API, Jira, Notion.

## Data & Machine Learning Engineer

CloudTalk, Prague, Czech Republic, *04/2022-06/2022*

* **Data engineering**. Development and implementation of the CD/CI pipeline for Keboola using kb-cli, GitHub, and GitHub Actions. Building ELT pipelines and data modelling. Building custom Extract/Load components for various Sales/CRM platforms (HubSpot, Crunchbase, Apollo) using the request library. Addressing data quality topics like entity profiling. Code reviewing.
* **Machine Learning**. Developing anti-fraud prediction models. Defined business logic with stakeholders and setting up alerting. Implementing cookiecutter framework for developing data science projects. Prepared a strategy for the ML infrastructure development. Code reviewing.
* **Data analytics**. Ad hoc reports using Jupyter Notebooks and Redash.
* Generally used **tools**. Python 3, Snowflake, ML Flow, MySQL, REST API, Jira, Outline. 

## Interim Product Owner of Personalization

Rohlik Group, Prague, Czech Republic, *09/2021-03/2022*

* **Distributed leadership** of the 4-member team (a back-end developer, a tester, a data analyst, a machine-learning engineer). Setting a roadmap, agile ceremonies (planning, grooming, retrospectives, etc.).
* **Feature Lifecycle Management** - collecting and prioritising business requirements based on their alignment with the company's business goals. Alignment with other relevant stakeholders across the company (including preemptive identification of dependencies, synergies, and blockers). Development and deployment of personalised features to production, including evaluating the business impact.
* Setting up **objectives**, **key results**, and key performance indicators of the Personalisation squad based on the company's OKRs (GTMHub). Reporting to the C-level management and the board of directors.

## Machine Learning Engineer

Rohlik Group, Prague, Czech Republic, *02/2021-03/2022*

* **Covered domains**: stakeholder management in the agile set-up (using Jira), data engineering, ML model development and deployment in production using Keboola and AWS (both batch and real-time predictions) and performance evaluation. Focus on the personalization of the product and CRM, solving various classification and regression tasks in the international context.
* **Data engineering**. Data models and ETL self-service in Keboola (including REST APIs via Postman) and AWS (S3, Redshift, MySQL).
* **MLOps using AWS** - Sagemaker, Lambda, CloudWatch, Glue, API Gateway.
* **A/B testing** - design, execution, and evaluation. Dashboards in Tableau, ad hoc reports using Jupyter Notebooks. Git using GitLab and AWS Codecommit.
* Other tools - Python (including various packages like pandas or seaborn), Snowflake, PySpark, TensorFlow.

## Data Analyst

Meiro, Brno, Czech Republic, *08/2020–11/2020*

* Covered domains: *Data Engineering, Data Analysis, Data Science*.
* **ETL pipelines management**, **Design of data models**.
* Integrating **PostgreSQL** and **MySQL**  with **R** and **Python**, data wrangling, description, and visualization to advanced statistical techniques and data science techniques (e.g. **recommendation engine** and **customer segmentation**) in **Python** using *VS Code*. Fundamentals of **Spark**.
* Linux desktop (Ubuntu), Bash, Git. Fundamentals of **Docker** and **CD/CI**.

## Data Analyst
Kiwi.com, Brno, Czech Republic, *04/2019–07/2020*

* Covered domains: *Product, Customer Experience, Customer Support*.
* Reports. Integrating **PostgreSQL, Snowflake**, and **BigQuery** with **R** and **Python**, data wrangling, description, and visualization to advanced statistical techniques (regression models, structural equation modelling) in R. Reporting in Markdown (**R notebooks, Jupyter Notebooks**, oral presentations).
* Dashboards. I’m proficient in working with tools like Looker, GoodData, Google Data Studio, or Retool (Plotly).
* Data engineering. Sense of data models and ETL self-service (Keboola, dbt).
* Linux desktop (Ubuntu), **Bash**, **Git**.
* **Jira**, **Scrum**.

## Lecturer
Masaryk University, Brno, Czech Republic, *09/2015–09/2020*

* Demonstrations of analyses and their interpretations, providing oral and written feedback in the following courses:
  * [Statistical Data Analysis I.](https://is.muni.cz/course/fss/spring2018/PSY117)
    * *Correlation, contingency tables, t-test, an introduction to non-parametric tests (e.g. chi-square)*.
  * [Statistical Data Analysis II.](https://is.muni.cz/course/fss/autumn2018/PSY252)
    * *Multiple linear regression, ANOVA, logistic regression, factor analysis, mixed effect models*.
  * [An Introduction to R](https://is.muni.cz/course/fss/autumn2019/PSYn5320)
    * An introduction to the **R language**, *data cleaning, wrangling, description and visualization, multivariate analyses in the R language.*

## Researcher
Transport Research Centre, Brno, Czech Republic, *06/2015–03/2019*

* Travel behaviour analyses, coordination of research activities, communication with the lay public as well as the [community of experts](https://link.springer.com/article/10.1007/s12544-018-0286-8).
* Investigator in the project [Česko v pohybu](https://www.ceskovpohybu.cz/). Development of the research tool, development and implementation of the probabilistic sampling procedure and algorithms for automated control of the data quality.
* Methodological and analytical supervision of the research project for the Czech Ministry of Transport related to the [Public opinion on autonomous vehicles](https://tots.upol.cz/artkey/tot-201902-0004_public-opinion-on-connected-and-automated-vehicles-the-czech-context.php).
* Project management of small project teams, e.g. program section for the conference [Dopravní chování v datech](https://www.cdv.cz/konference-dopravni-chovani-v-datech-2018/).

# Education

## Psychology (Ph.D.)
Masaryk University, Brno, Czech Republic, *09/2015–06/2022*

* Specialisation in advanced analytical methods – [structural equation modelling](https://digitalcommons.ciis.edu/cgi/viewcontent.cgi?article=1516&context=ijts-transpersonalstudies), [multinomial regression](https://www.tots.upol.cz/pdfs/tot/2019/01/03.pdf).
* Member of the *Disciplinary Committee — Faculty of Social Studies*.
* Thesis: [Intention to Use Autonomous Vehicles](https://is.muni.cz/th/v62um/)
* Graduated with [honours](https://is.muni.cz/student/vystavene_znamky?lang=en;setlang=en;studium_osoby=896290).

## Psychology (Master degree)
Masaryk University, Brno, Czech Republic, *2013–2015*

* Specialisation in **frequentist statistics**, introduction to Bayesian statistics.
* Scholarship for excellent performance in research activities given by the Faculty of Social Studies during the academic year 2013/2014.
* Thesis: [Student Styles Questionnaire in the Czech context: A pilot study](https://is.muni.cz/th/ll1m4/?lang=en)
* Graduated with [honours](https://is.muni.cz/student/vystavene_znamky?lang=en;studium_osoby=665097).

## Psychology and Sociology (Bachelor degree)
Masaryk University, Brno, Czech Republic, *2010–2013*

* Specialisation in quantitative methodology and statistical data analysis in IBM SPSS with focus on linear models.
* Scholarship for best students of Faculty of Social Studies during the academic years 2010/2011 and 2011/2012.
* Thesis: [The relationship between spirituality and autonomy in late adolescence and emerging adulthood](https://is.muni.cz/th/nldap/?lang=en).
* Graduated with [honours](https://is.muni.cz/student/vystavene_znamky?lang=en;studium_osoby=544544).

# Internships
Danmarks Tekniske Universitet, Management Engineering, Lyngby, Denmark. *08-09/2017*

Corpus Christi College, University of Cambridge, Cambridge, UK. *07-09/2015*

# Selected publications

* Gabrhel, V., Ježek, S. (2017). Factor validity and internal consistency of the Expressions of Spirituality Inventory – Revised (ESI-R): The Czech context. International Journal of Transpersonal Studies, 36 (1), 101-109. doi:10.24972/ijts.2017.36.1.101.
* Gabrhel, V. (2019). Feeling like cycling? Psychological factors related to cycling as a mode choice. Transactions on Transport Sciences, 10 (1). doi:10.1007/978-3-319-60441-1_80
* Gabrhel, V., Ježek, S., Havlíčková, D. (2019). Public opinion on connected and automated vehicles: the Czech context. Transactions on Transport Sciences, 10 (2). doi:10.5507/tots.2019.011
* Havlíčková, D., Gabrhel, V., Adamovská, E., Zámečník, P. (2019). The role of gender and age in autonomous mobility: general attitude, awareness and media preference in the context of Czech Republic. Transactions on Transport Sciences, 10 (2). doi:10.5507/tots.2019.013
* Gabrhel, V., Ježek, S., & Zámečník, P. (2021). Driving locus of control: the Czech adaptation. Czechoslovak Psychology, 65(1), 86-100. doi: https://doi.org/10.51561/cspsych.65.1.86.
* Jakopec, A., Gabrhel, V., Keane, L., Kovač, N., Reigbert, K., Andersen, T. L. (2015). Work Engagement and Performance: Does the (Mis)Alignment of Justice Sources Matter? Journal of European Psychology Students, 6 (2), 75–78. doi:10.5334/jeps.cs.
* Kurečková, V., Gabrhel, V., Zámečník, P., et al. (2017) First aid as an important traffic safety factor – evaluation of the experience–based training. European Transport Research Review, 9 (5). doi:10.1007/s12544-016-0218-4/
* Šimeček, M., Gabrhel, V., Tögel, M., & Lazor, M. (2018). Travel behaviour of seniors in Eastern Europe: a comparative study of Brno and Bratislava. European Transport Research Review, 10 (16), 1-8. doi:10.1007/s12544-018-0286-8
* Švarcová, J., Gabrhel, V. (2014). Educational Mobility and Educational Aspirations of High School Students in the Czech Republic. The International Journal of Interdisciplinary Educational Studies, 8 (2), 1-12.
* Švarcová, J., Harantová, L., & Gabrhel, V. (2013). Statistical analysis of student’s interest in creative professions in the Czech Republic. International Journal of Mathematical Models and Methods in Applied Sciences, 7 (2), 444-451.
* Thapa, D., Gabrhel, V., & Mishra, S. (2021). What are the factors determining user intentions to use AV while impaired? Transportation Research Part F: Traffic Psychology and Behaviour, 82, 238-255. doi: 10.1016/j.trf.2021.08.008.

# Selected talks

* Gabrhel, V. (2018). Public opinion on automated vehicles: the Czech context. Paper presented at the conference Silniční konference 2018 in Ostrava, Czech Republic.
* Gabrhel, V., Kouřil, P., & Lazor, M. (2017). Česko v pohybu. Paper presented at the conference Silniční konference 2017 in Brno, Czech Republic.
* Gabrhel, V., Šenk, P., Lazor, M., & Ondráčková, J. (2016). Travel Behavior in Central and Eastern Europe: a Bratislava Case Study. Paper presented at the World Conference on Transport Research – WCTR 2016 Shanghai in Shanghai, People’s Republic of China.
* Gabrhel, V. (2018). Public opinion on automated vehicles: the Czech context. Paper presented at the conference Silniční konference 2018 in Ostrava, Czech Republic.
* Gabrhel, V., Zámečník, P. (2018). Concerns related to the interaction between autonomous vehicles and other transport systém users. Paper presented at the conference Road Safety on Five Continents in Jeju, Republic of Korea.
* Šimeček, M., Gabrhel, V., Togel, M., & Lazor, M. (2016). Travel behaviour of seniors in Central Europe: a comparative study of Brno and Bratislava. Paper presented at the conference NECTAR Joint Cluster 2 and Cluster 3 International Workshop “The role of planning towards sustainable urban mobility” in Brno, Czech Republic.

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