Data Scientist

Role overview

As a Data Scientist on the Housing team, you will convert rich property and market data into dependable products and insights that serve consumers and real estate professionals. The position blends product thinking, business impact, and market analysis, with data from listings and sold properties used to support customer-facing tools and reinforce the company’s standing as a trusted market source through evidence-based insights and publications. You will work as a strategic analytical partner, making sure outputs are solid, relevant in practice, and easy for stakeholders to turn into action.

What you will work on

You will contribute to housing data products that improve transparency and build trust for users and agencies, including valuation and estimation journeys that support the estimation funnel, realtor prospecting and lead-support tooling such as fuzzy geo-localised seller lead maps, and sold-property datasets that help users compare comparable transactions before making selling or buying decisions. Your work will also support commercial outcomes such as higher listing conversion and stronger seller lead generation.

You will also help create market-facing insights and content that strengthen the brand as an industry reference. This includes monthly and quarterly barometers, local market outlooks, ad hoc studies on emerging themes and public-policy questions, and support for press queries, market-data requests, and narrative development when spokesperson-ready messaging is needed.

Core responsibilities

Analytical quality is a major part of the role. You will verify and pressure-test results, identify outputs that are inconsistent or misleading, and confirm that findings make sense in the real world. You will turn analysis into practical recommendations, guidance tools, and clear takeaways for non-technical stakeholders. Collaboration is equally important: you will work across disciplines with economists, product managers, business teams, and data engineers to move initiatives from concept to measurable impact.

Skills and experience expected

You should bring hands-on technical capability in Python, including pandas, numpy, data cleaning and transformation, exploratory analysis, and basic visualisation with matplotlib or seaborn. A basic working knowledge of SQL is also needed for extracting data, joining tables, filtering records, and aggregating results. You should be comfortable handling large structured datasets and using reproducible workflows such as scripts, notebooks, documentation, and version control.

On the analytical side, the role calls for statistical analysis, hypothesis testing, regression and other predictive modelling approaches, and clustering. A solid foundation in econometrics is important, including linear models, an intuitive understanding of panel data, and awareness of bias. Strong data quality assessment and the ability to judge results through a practical, real-world lens are essential.

From a working style perspective, you should be able to explain complex findings clearly to non-technical audiences, enjoy influencing decisions through data, and bring collaboration, ownership, initiative, and follow-through from idea to analysis to impact.

Language requirements

Excellent English is required. French and/or German would be a strong advantage.

Compensation

The total compensation for this position is up to EUR 65,000 gross per year.