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

InPost is a leading e-commerce parcel delivery platform in Europe, operating a network of almost 47,000 Automated Parcel Machines (APMs) and nearly 35,000 pick-up drop-off points across nine countries. Founded in 1999, InPost offers delivery and fulfilment services focusing on flexible, environmentally friendly, and contactless parcel delivery.

• Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, or Econometrics.
• At least 3 years of commercial experience as a Data Scientist; consulting and marketing analytics experience is a plus.
• Goal-oriented, independent mindset with skills in change and time management, business consciousness, and long-term problem decomposition.
• Proficiency in Polish and English languages.
• Excellent knowledge of ML solutions including clustering, recommender systems, regression, and classification.
• Hands-on experience working with large data sets.
• Proficiency in Python 3 and ML/data analysis libraries like Pandas, Numpy, Scipy, Scikit-learn, Statsmodels, TensorFlow, and PyTorch.
• Experience in writing well-structured code using functions, classes, and modules.
• Knowledge and experience with PySpark, relational databases, and cloud platforms such as Databricks, Azure, Google Cloud Platform (GCP), AWS, and Snowflake.

Nice to have:

• Experience leveraging CI/CD pipelines in data products.
• Experience with data pipelines framework, preferably Kedro.
• Experience with CLI tools like bash or zsh.

• Partner with Product and Business Teams to understand needs and deliver actionable data science solutions for products like InPost Mobile, Loyalty Programme, and InPost Pay.
• Develop and implement ML models, GenAI products, hybrid approaches, and data analytics to optimize marketing and product strategies.
• Collaborate with Data & AI teams and Technology teams for seamless integration of data initiatives.
• Explore and stay updated on cutting-edge methods and trends in Data Science and AI.
• Communicate insights and recommendations effectively to management, business teams, and data community members.

Summary of end-to-end data product activities:

• Explore business needs and analyze problems.
• Develop solutions using ML/AI hands-on skills.
• Present insights and results clearly to non-technical audiences.
• Maintain solutions with basic BI skills and model monitoring.