Rafał Prońko

in the project, I am responsible for:
1) Lead the DS/ML projects
2) Creating the data infrastructure - create the data models, creating and managing the ETL process with Airflow, Beam, Prefect, Feast, Seldon
3) Mentoring the team

Working on projects as a Lead Data Scientist.

in the project, I am responsible for:
1) Lead the DS/ML projects
2) Creating the data infrastructure - create the data models, creating and managing the ETL process with Airflow and Beam
3) Mentoring the team

Working on projects like:
1) Creating the data infrastructure - create the databases in Postgresql, creating the data warehouse on Google BigQuery, creating and managing the ETL process with Airflow on Google Composer, Beam
2) Data Scientist - find the pattern in structured and unstructured data from CV's - find the pattern for tenure prediction, normalize the skills and job titles working with FastText, LightGBM, ADASYN, TomekLink - build own language model with Spacy based on data from the resumes.
3) BI - creating the reports for business in tableau and datastudio
4) Making the stress tests with locust, make the recommendation of what should be improved
5) Manage / Mentoring the team
6) Creating the plan for DS / ML projects in the company
7) Deploy the models with the Google Prediction
8) Using XAI methods for the production models to explain the decision.

Working on projects like:
1) Creating the data infrastructure - create the databases in Postgresql, creating the data warehouse on Google BigQuery, creating and managing the ETL process with Airflow on Google Composer
2) Data Scientist - find the pattern in structured and unstructured data from CV's - find the pattern for tenure prediction, normalize the skills and job titles working with FastText, LightGBM, ADASYN, TomekLink,...
3) BI - creating the reports for business in tableau and datastudio
4) Making the stress tests with locust, make the recommendation of what should be improved

Working on projects related to:
1) Computer vision - real-time video analysis / deep learning / OpenCV / Sklearn image /pytorch - like face recognition / face spoofing recognition mechanism / object detection / object localisation
2) Analysing and learning from graph information - find the pattern in graph data / search graph for new interesting connection
3) deep text analysis to find pattern / categorisation
4) Blockchain economy, token design, token engineering, ICO preparing, token generation, governance mechanism in the decentralized apps, incentivization mechanism for decentralized apps

Also, I was a speaker on Data Scientist Summit 2018 and Big Data conference in Warsaw (have a 1-day tutorial about basic NLP with Big Data on SPARK)