InPost has revolutionised e-commerce parcel delivery in Poland and is now one of Europe’s leading OOH e-commerce enablement platforms. Founded in 1999 by Rafał Brzoska, InPost provides delivery services through our network of almost 47,000 Automated Parcel Machines (APMs) and almost 35,000 pick-up drop-off points (PUDO) in nine countries across Europe, as well as to-door courier and fulfilment services to e-commerce merchants. InPost’s lockers provide consumers with a cheaper and more flexible, convenient, environmentally friendly and contactless delivery option.
We are seeking talented and passionate Data Scientists to join our Personalization Hub. In this role, you will leverage advanced analytical techniques and machine learning models to extract actionable insights from our complex data sets, driving strategic decision-making and operational excellence. You will collaborate closely with cross-functional teams to develop innovative solutions that enhance our logistics operations, improve efficiency, and elevate the customer experience.
If you have specialized knowledge in user, product and/or marketing data science (or want to gain it) - we would very much like to meet you! :)
Job DescriptionOn a daily basis you will:
- Partner with our Product and Business Teams to understand their needs, translate them into data science solutions, and provide actionable insights. We work with all business products within InPost, e.g. InPost Mobile, Loyalty Programme, InPost Pay, among others.
- Develop and implement data science solutions (ML models, GenAI products, hybrid approaches, data analytics) to optimize marketing and products strategies, enhance user experience and shape targeting.
- Collaborate closely with cross-functional teams (e.g. other Data&AI teams, Technology teams) to ensure seamless integration of data-driven initiatives.
- Stay ahead of the curve exploring cutting-edge methods and being on top of new trends in Data Science & AI.
- Communicate insights and recommendations to the management and business teams, and other data community members.
In summary – you will have an opportunity to conduct end-to-end data products: exploring business needs (skills: understanding business, communication), analyze problems and propose thesis (data analytics), develop a solution (skills: hands-on ML/AI), present insights and results (skills: communication, translating technical stuff to non-technical people, ppt/BI), maintain the solution (skills: basic BI skills, model monitoring).
QualificationsJob requirements:
- Education – Bachelor’s or Master’s degree in a relevant field, e.g. Data Science, Computer Science, Mathematics, Econometrics
- Experience – you have at least 3 years of commercial experience as a Data Scientist. Consulting and marketing analytics experience are a plus
- Mindset – you are goal-oriented and independent, skilled in change and time management, business-conscious, able to think long-term and decompose business problems
- Languages – you are proficient in Polish and English (other languages knowledge is a plus).
Technical skills:
- Excellent knowledge of ML solutions and their impact on business and user experience (clustering, recommender systems, regression, classification, etc.).
- Hands-on experience with working with large amounts of data.
- Proficiency in Python 3, as well as ML and data analysis libraries (e.g. Pandas, Numpy, Scipy, Scikit-learn, Statsmodels, TF/Pytorch, etc.).
- Experience in writing well-structured code: functions, classes, modules.
- Knowledge and experience in PySpark, relational databases, cloud solutions (e.g. Databricks, Azure, GCP, AWS, Snowflake).
Nice to have:
- Experience in leveraging CI/CD pipelines in data-based products.
- Experience with data pipelines framework, preferably Kedro.
- Experience with CLI tools: bash/zsh.
Why join Us?
- Impact: Your work will directly influence strategic decisions and operational efficiencies across multiple international markets.
- Innovation: Be part of the team that's pushing boundaries of data analytics, working with the latest technologies and methodologies.
- Growth: This role offers unparalleled opportunities for professional development in a data-driven, technology-forward environment.
- Collaboration: Engage with cross-functional teams, share knowledge and best practices, fostering a culture of continuous learning and improvement.
