Requirements

Expected technologies

Python

Databricks

Microsoft Azure Environment

SQL

Operating system

Windows

Linux

Our requirements

• 3–5 years of industry experience applying advanced analytics techniques in a heavy operation industry,
• Strong analytical and problem-solving skills, with experience in Classical ML methods and discrete choice modeling,
• Proficient in Python and machine learning libraries, with strong experience using Databricks for scalable analytics,
• Experience with statistical methods, decision analytics, and revenue optimization,
• Intermediate understanding of Large Language Models (LLMs) and Generative AI, with practical exposure to their application in enhancing analytics workflows or decision support systems,
• A data scientist mindset with a strong sense of curiosity—actively seeks the meaning behind data patterns and continuously explores opportunities to align data science solutions with business processes and strategic goals,
• A strategic mindset, capable of transforming complex datasets into actionable insights,
• Strong communication skills, with the ability to translate complex data insights into clear, actionable recommendations for stakeholders across various business functions,
• Ability to collaborate effectively in a fast-paced, cross-functional team environment,
• Familiarity with SQL, database management and Microsoft Azure Environment is an advantage,
• Good knowledge of English in speaking and writing (B2/C1),
• working 2 days per week in Gdańsk office.

Optional

• Demonstrated expertise in passenger choice modeling development is a significant advantage,
• Knowledge about Agile/Scrum methodologies.

Your responsibilities

• Develop and refine ML models in airline commercial processes to improve airline pricing, route planning, and revenue strategies,
• Re-evaluate and challenge enhance passenger choice models to ensure optimal performance, robustness, and alignment with evolving market dynamics ,
• Contribute to PoC (Proof of Concept) and implementation of GenAI use cases across commercial and admin domains,
• E2E deployment of Data Science project ETLs in Microsoft Azure environment,
• Collaborate with business units and ensure close communication with Business end users by translating analytical findings into impactful commercial strategies,
• Support machine learning and statistical model deployment, ensuring seamless integration into operational systems,
• Conduct in-depth data analysis on airline revenue management, pricing strategies and customer segmentation,
• Present actionable insights through compelling reports, data visualizations and presentations to leadership,
• Preparing documentation of the particular steps and the final product.