Position: Machine Learning Engineer / Data Engineer
Category: Data Science
English level: fluent
Years of experience: 6

Designed a serverless ML cloud architecture from scratch (AWS Cloud Formation/Terraform)
FleetyFly:
- Deployment and automatization of ML pipelines that optimize fleet efficiency and increase
clients revenue
- Design serverless ML cloud architecture from scratch (AWS Cloud Formation/Terraform)
- Prepared CI/CD pipeline for ML projects (AWS CodeBuild)
- Strategic decision about company data architecture
Wind Mobility GmbH, Department: Data.
- Created e-scooters rebalancing model that increases the revenue for the rebalanced e-scooters
by 50%
- Creating serverless ML architecture in AWS cloud (AWS Cloud Formation)
- Creation and deployment of ML models to production environment (Python, SparkSQL, AWS
Fargate)
- Data Engineering: moving data from operational to analytical DBs in serverless fashion (AWS
Glue, AWS Lambda, DataBricks), extensive unit/integration testing
- Optimization of complex logistics systems that increases numbers of rides in a city by up to
25%
Vueling Airlines, S.A., Department: Data.
- Created a model that predicts price and pax of every flight in the 3 months period with daily
accuracy below 10 % MAPE
- Developed and implemented end to end data science pipelines from scratch for supervised
and unsupervised ML models (PySpark/Python)
- Was part of Data Engineering team that deploys and automates ML models to production
env. Provided logging and monitoring pipelines (Python, MLFlow)
Lidl Stiftung & Co. KG, Department: Advanced Analytics.
- Developed ML algorithms (regression and classification) (sk-learn)
- Performed exploratory data analysis and business analysis, data visualization (R, Python,
SQL)
- Improvement of sales model (association analysis etc.
Most important is to work on interesting projects, and hands on work on developing ML solution, deploying architecture for ML and data. I would prefer keep the meetings to minimum (of course some meetings are necessary, but some aren't :) ). Working with experienced people from which I can learn, or with whom I can brainstorm is beneficial of course, but I also have experience with working in a startup where I was the first DS/ML Eng.