Agile Data Scientist

TL;DR

Agile Data Scientist (ML): Designing and implementing robust machine learning models and managing end-to-end ML workflows with an accent on gradient boosting techniques and cloud deployment. Focus on building scalable data pipelines, extracting insights from complex datasets, and ensuring model reproducibility.

Location: Hybrid in Budapest, Hungary

Company

hirify.global Group is an IT consulting and services company, founded in 1999, focusing on financial services, telecommunications, automotive, and energy sectors.

What you will do

• Collect, clean, and prepare data from SQL/NoSQL sources.
• Design and implement ML models, especially gradient boosting.
• Manage end-to-end ML workflows including preprocessing, training, inference, and prediction.
• Visualize and analyze data for business decisions using Power BI or Looker.
• Deploy and maintain models and pipelines on cloud platforms (AWS, Azure, GCP).
• Apply Agile methods, take ownership, and solve problems independently.

Requirements

• Advanced Python and Jupyter Notebook skills.
• Experience with SQL & NoSQL databases (Oracle, Impala; BigQuery/MongoDB a plus).
• Strong knowledge of machine learning algorithms, especially gradient boosting (XGBoost, CatBoost).
• Proven end-to-end ML workflow experience (data ingestion, preprocessing, training, inference, prediction).
• Proficiency in data visualization tools (Power BI, Looker).
• Experience with cloud platforms (AWS, Azure, GCP) and version control (Git) with CI/CD pipelines.

Nice to have

• PySpark knowledge.
• Recommendation engine development experience.
• Apache Airflow knowledge.
• Experience in telecommunications or SME sector.

Culture & Benefits

• Open communication, transparency, and team spirit.
• Welcomes fresh ideas and supports continuous development.
• Provides a stable background and continuous learning opportunities.