You will take on technical leadership and end-to-end ownership for our Pricing/Revenue-ML topics—with a clear focus on measurable impact. You will work closely with Product and Engineering, define measurability/experiments, and ensure that our models not only “look good” but also perform reliably in practice.
Important: No disciplinary personnel responsibility. You lead through expertise, standards, and ownership.
Your ResponsibilitiesEnd-to-End Ownership: You are responsible for the entire lifecycle of pricing and revenue topics—from hypothesis to implementation to measurable evaluation. Your focus: Clear business uplift.
Smart Modeling: You develop and optimize forecasting and pricing models. You pragmatically decide which method gets us to the goal fastest and most stably.
Signal Expertise: You manage time series, demand signals, and heterogeneous data sources. You ensure that features and labels are defined absolutely clean and “leakage-proof.”
Experimentation Framework: You build a robust measurement system (holdouts, A/B tests, guardrails) and define crystal-clear criteria for rollout decisions.
Engineering-Grade ML: You establish standards for backtesting, reproducibility, and versioning. For us, it's: Engineering quality instead of notebook-only.
MLOps Best Practices: You bring MLOps best practices and drive continuous improvement in our ML workflow.
Reliable Operations: You ensure operations through smart monitoring, drift detection, and pragmatic retraining mechanisms.
Automation & Scale: You automate high-leverage processes (backtests, monitoring checks) to massively increase throughput and quality.
Data Foundation: Where it makes sense, you design data models directly in the warehouse (Snowflake/dbt) as a basis for reliable metrics and features.
Full Transparency: You standardize dashboards (e.g., Metabase) for our business KPIs and ensure the data quality is beyond reproach.
Stakeholder Sparring: You prioritize requirements together with Product & Revenue and translate them into ML solutions. Your motto: Impact over output.
Deep Experience: You have 6+ years of experience in applied ML engineering or data science – ideally directly in a product or business context.
Proven Impact: You have already achieved demonstrable success in the areas of pricing, revenue, forecasting, or similar “money systems.”
Evaluation Pro: You think offline vs. online, immediately recognize bias/leakage, and master the fundamentals of robust metrics and guardrails.
Tech Stack: Your Python and SQL skills are production-level (testable, versioned, reproducible).
Startup DNA: You love the 80/20 principle, work extremely pragmatically, and want full ownership for your topics.
Language Skills: You communicate fluently and confidently in English.
Hands-On MLOps & Cloud: Bonus if you have hands-on skills to work with MLOps tooling and cloud infrastructure, e.g. AWS
Domain Knowledge: Experience in revenue management or dynamic pricing (e.g., travel, mobility, eCommerce).
Demand Understanding: You know how seasonality, events, and lead times affect pricing.
Modern Toolchain: You are proficient in analytics engineering (dbt, Snowflake, Metabase) and know how to build a clean data foundation.
Skills Required
- 5+ years relevant experience in ML Engineering, Data Science, or Analytics
- Production-level Python and SQL skills
- Fluency in German and English
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.
