(Senior) Data Scientist - Personalisation (m/f/n)

InPost is a leading European e-commerce enablement platform providing delivery services through a network of Automated Parcel Machines and pick-up drop-off points across nine countries. The company focuses on innovative, flexible, and environmentally friendly parcel delivery solutions.

• Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Econometrics, or related field.
• At least 3 years of commercial experience as a Data Scientist; consulting and marketing analytics experience is a plus.
• Goal-oriented, independent, skilled in change and time management, business-conscious, and able to decompose business problems.
• Proficient in Polish and English; other languages are a plus.
• Excellent knowledge of ML solutions such as clustering, recommender systems, regression, and classification.
• Hands-on experience working with large data sets.
• Proficiency in Python 3 and ML/data analysis libraries (Pandas, Numpy, Scipy, Scikit-learn, Statsmodels, TensorFlow/PyTorch).
• Experience writing well-structured code using functions, classes, and modules.
• Knowledge and experience with PySpark, relational databases, and cloud solutions (Databricks, Azure, GCP, AWS, Snowflake).

Nice to have:

• Experience leveraging CI/CD pipelines in data products.
• Experience with data pipeline frameworks, preferably Kedro.
• Experience with CLI tools such as bash or zsh.

• Partner with Product and Business Teams to translate needs into data science solutions and provide actionable insights.
• Develop and implement data science solutions including ML models, GenAI products, hybrid approaches, and data analytics to optimize marketing and product strategies.
• Collaborate with Data & AI teams and Technology teams to integrate data-driven initiatives.
• Explore cutting-edge methods and stay updated on new trends in Data Science and AI.
• Communicate insights and recommendations to management, business teams, and data community members.

Summary: Conduct end-to-end data product development including business needs exploration, problem analysis, solution development, presentation of insights, and solution maintenance.