As a Senior Machine Learning Engineer, you will work in the Pricing & Revenue context, focusing on data-driven improvements for forecasting, pricing logic, and product insights. You will develop analyses and models that demonstrate measurable impact in our product – with thorough evaluation, a solid data foundation, and pragmatic implementation. You will collaborate closely with Product & Engineering.
Your ResponsibilitiesModel Evolution: You will develop and optimize our forecasting and pricing models as well as data-driven decision logics, always with methodical pragmatism and a strong focus on impact.
Signal Hunting: You will work with time series, demand signals, and heterogeneous data sources. You define features and labels carefully to leave no chance for leakage.
Measurement & Guardrails: You are responsible for evaluation through backtesting, robust metrics, and segmentation. You support holdouts and A/B logics and maintain the balance between offline and online performance.
ML Engineering Best Practices: You raise standards for backtesting, reproducibility, and versioning. For us, it's: Engineering quality instead of notebook-only.
Data Visibility: You enhance dashboards and reports that make model and business KPIs transparent. Your focus is always on the highest data quality.
Smart Workflows: You drive reproducible workflows (versioning, clear pipelines, meaningful tests) and automate recurring analyses and evaluation runs.
Track Record: You have 4+ years of experience in data science or applied ML engineering – ideally directly in a product or business context.
Data Intuition: You possess extremely strong SQL skills and a deep sense for data quality, debugging, and consistent metrics.
Python Pro: Your Python code is clean and your analyses are transparent. Initial experience with product-oriented setups is a big plus.
Experiment Mindset: You master the basics of bias/leakage-awareness and know how to think in guardrails and offline-vs-online scenarios.
Work Style: You take full ownership of your topics. You work according to the 80/20 principle (pragmatic!), are reliable, and communicate clearly.
Entrepreneurial Spirit: You think entrepreneurially and want to truly make a difference.
Language Skills: You communicate fluently and confidently in English.
Nice-to-haves – What Sets the Best Apart
Domain Expertise: You already have experience in revenue management or dynamic pricing (e.g., hotel, travel, eCommerce, or mobility).
Demand Knowledge: You are familiar with seasonality, events, lead times, and segment patterns.
Modern Stack: You have already worked with analytics engineering or warehouse tools such as dbt, Snowflake, or Met
Hands-On MLOps & Cloud: Bonus if you have hands-on skills to work with MLOps tooling and cloud infrastructure, e.g. AWS
Skills Required
- 4+ years of experience in data science or applied machine learning engineering
- Extremely strong SQL skills
- Strong data quality, debugging, and consistent metrics skills
- Strong Python programming skills with clean, transparent code
- Understanding of bias, data leakage, guardrails, and offline-versus-online evaluation
- Fluency in German
- Good English skills
- Experience in a product or business context
- Experience with revenue management or dynamic pricing
- Familiarity with seasonality, events, lead times, and segment patterns
- Experience with analytics engineering or warehouse tools such as dbt, Snowflake, or Metabase
- Hands-on MLOps tooling and cloud infrastructure experience, such as AWS
What We Do
Elevate your hotel's financial performance with our intelligent revenue management software. Tailored for the hospitality industry, it offers dynamic pricing capabilities to help you adjust rates effectively, ensuring you maximize revenue.
