Senior Data Scientist/ML Ops Engineer (all genders)

Responsibilities With a mission to empower 15.6 million house owners across Germany and the broader DACH region, we at Aroundhome are building a platform where every strategic decision is powered by data. We’re a team of around 250 people, and to take the next step, we’re looking for an experienced and hands-on Senior Data Scientist (all genders) to bring machine learning into the heart of our product. As a Senior Data Scientist, you will build Aroundhome’s first machine learning model in production: the logic that decides which partners each homeowner is matched with. You will start from the problems homeowners and partners actually have, and own the solution end to end, in a small new team with a product manager, an engineer and the team lead. Responsibilities: • Start from the Customer: Understand what homeowners and partners need from a match, and turn that into the outcomes and guardrails your models optimize for, not just model metrics. • Own Our Matching Models: Build and run the models behind our partner matching, end to end: data, features, training, evaluation, deployment and monitoring. • Make Every Match Count: Improve our matching logic with deployed models that make the most efficient use of partner capacity, raise match rates, and account for regional supply and demand. • Prove Impact: Measure every improvement against our current logic in controlled experiments designed for a two-sided marketplace, before it scales. • Build the Data Foundation : Create the datasets and pipelines your models need, together with our engineer and the Data Engineering team. In the first months, this is a big part of the role. • Shape Our ML Stack: Decide with the team how and where models run, and set the practices for machine learning at Aroundhome. • Translate Models into Decisions: Explain trade-offs between customer experience, partner fulfillment and revenue to product managers, partner category managers, key account managers and senior leadership. Skills • Bachelor’s or Master’s degree or equivalent practical experience in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Economics). • Proven experience (5+ years) in applied data science, including at least one ads, ranking, pricing or marketplace model you took to production. • Production-grade Python and advanced SQL, plus hands-on experience with modern data tooling (e.g., dbt, Airflow, Spark). • Experience deploying and monitoring models on a cloud ML platform (e.g., SageMaker, Vertex AI, MLflow). • Solid foundation in experiment design and causal inference, ideally in a marketplace where test and control groups affect each other. • Hands-on experience with classical machine learning (e.g., gradient-boosted trees), probability calibration and offline vs online evaluation. • A product mindset: you define success in homeowner and partner outcomes, challenge assumptions with evidence, and add complexity only where it earns its place. • Strong communication skills and the ability to explain models and trade-offs to technical and non-technical stakeholders. • A plus: experience with recommender systems or search ranking. What we offer Hybrid Work // Stay flexible: Decide freely whether you want to work from our office in Berlin, from home throughout Germany, or fully remote from Portugal. Additionally, we offer you the possibility to work up to 30 days per year from selected EU countries. Room to grow // Stay curious: In regular/annual feedback meetings, you design your own personal development path together with your team lead. In addition, we offer monthly Tech Talks, an annual training budget, free access to LinkedIn Learning as well as great workshops to seek new input and specific training for your personal development. Mental & ph