Staff Machine Learning Engineer - Pricing & Revenue (m/f/d)

YOUR ROLE 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 RESPONSIBILITIES - End-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. YOUR PROFILE - 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. BONUS POINTS (NICE-TO-HAVES) - 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.