Data Science Analyst

Our client is looking for a proactive and detail-oriented analyst passionate about data modeling and advanced analytics to translate complex business problems into clear analytical solutions. The role is within the Advanced Analytics team, focusing on building analytical models, segmentation, profiling, scoring, and predictive solutions to support strategic business goals. The analyst will work closely with product and business teams, delivering actionable insights and interactive analytical applications built in Streamlit.

• Experience in advanced data analytics including profiling, segmentation, scoring, predictive modeling, and developing analytical models supporting strategic decision-making.
• Skilled in designing, maintaining, and processing large datasets (Data Marts), applying statistical and analytical methods to identify trends and key business drivers.
• Advanced SQL skills including query optimization for large datasets in Snowflake.
• Practical knowledge of Python and analytical libraries (Pandas, NumPy, Matplotlib, Scikit-learn).
• Experience building analytical applications in Streamlit and using Git.
• Strong analytical thinking, problem-solving skills, and data interpretation abilities.
• Language skills: Polish (C2), English (B2).

Nice to have:

• Snowflake Badge 1: Data Warehousing Workshop.
• Experience with PySpark or Snowpark.
• Knowledge of dbt.

• Translating business requirements into analytical solutions by analyzing needs, designing solutions, prototyping reports, and aligning with stakeholders.
• Creating interactive analytical applications in Streamlit including geographic, trend, and KPI visualizations, continuously improving usability and performance.
• Conducting exploratory data analysis using Python and SQL on large datasets to identify trends, anomalies, and improvements, and formulating strategic recommendations.
• Processing and integrating large volumes of data in Snowflake and dbt, creating and maintaining data sources, optimizing processes using SQL and Python.
• Monitoring the quality and performance of analytical solutions and driving continuous improvements.
• Creating and updating technical and business documentation in Confluence to ensure continuity of analytical operations.