Senior Data Scientist/ML Ops Engineer (all genders)

<h2>Responsibilities</h2> <p>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.</p> <p>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.</p> <h2>Responsibilities:</h2> <ul> <li><strong>Start from the Customer:</strong> 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.</li> <li><strong>Own Our Matching Models:</strong> Build and run the models behind our partner matching, end to end: data, features, training, evaluation, deployment and monitoring.</li> <li><strong>Make Every Match Count:</strong> 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.</li> <li><strong>Prove Impact:</strong> Measure every improvement against our current logic in controlled experiments designed for a two-sided marketplace, before it scales.</li> <li><strong>Build the Data Foundation</strong>: 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.</li> <li><strong>Shape Our ML Stack:</strong> Decide with the team how and where models run, and set the practices for machine learning at Aroundhome.</li> <li><strong>Translate Models into Decisions:</strong> Explain trade-offs between customer experience, partner fulfillment and revenue to product managers, partner category managers, key account managers and senior leadership.</li> </ul> <h2>Skills</h2> <ul> <li>Bachelor’s or Master’s degree or equivalent practical experience in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Economics).</li> <li>Proven experience (5+ years) in applied data science, including at least one ads, ranking, pricing or marketplace model you took to production.</li> <li>Production-grade Python and advanced SQL, plus hands-on experience with modern data tooling (e.g., dbt, Airflow, Spark).</li> <li>Experience deploying and monitoring models on a cloud ML platform (e.g., SageMaker, Vertex AI, MLflow).</li> <li>Solid foundation in experiment design and causal inference, ideally in a marketplace where test and control groups affect each other.</li> <li>Hands-on experience with classical machine learning (e.g., gradient-boosted trees), probability calibration and offline vs online evaluation.</li> <li>A product mindset: you define success in homeowner and partner outcomes, challenge assumptions with evidence, and add complexity only where it earns its place.</li> <li>Strong communication skills and the ability to explain models and trade-offs to technical and non-technical stakeholders.</li> <li>A plus: experience with recommender systems or search ranking.</li> </ul> <h2>What we offer</h2> <p><strong>Hybrid Work // Stay flexible:</strong> Decide freely whether you want to work from our office in Berlin, from home throughout Germany,