Your Role
As a
Senior Machine Learning Engineer, you will play a crucial role in the
Pricing & Revenue context, focusing on the development and optimization of forecasting, pricing logic, and product insights. Your responsibilities will span from model development and optimization to signal hunting, measurement and guardrails, and maintaining best practices in machine learning engineering. You will work closely with Product & Engineering teams to ensure that your analyses and models are effective, data-driven, and measurable.
Your Responsibilities
- Develop and optimize forecasting and pricing models as well as data-driven decision logics with methodical pragmatism and a strong focus on impact.
- Work with time series, demand signals, and heterogeneous data sources to define features and labels carefully, leaving no chance for leakage.
- Evaluate models through backtesting, robust metrics, and segmentation, supporting holdouts and A/B logics, and maintaining the balance between offline and online performance.
- Raise standards for backtesting, reproducibility, and versioning, focusing on engineering quality instead of notebook-only.
- Enhance dashboards and reports that make model and business KPIs transparent, prioritizing the highest data quality.
- Drive reproducible workflows (versioning, clear pipelines, meaningful tests) and automate recurring analyses and evaluation runs.
Your Profile
- 4+ years of experience in data science or applied machine learning engineering, ideally in a product or business context.
- Strong SQL skills and a deep sense for data quality, debugging, and consistent metrics.
- Python code is clean, and analyses are transparent. Initial experience with product-oriented setups is a big plus.
- Master the basics of bias/leakage-awareness and know how to think in guardrails and offline-vs-online scenarios.
- Take full ownership of your topics, work according to the 80/20 principle, are reliable, and communicate clearly.
- Think entrepreneurially and want to truly make a difference.
- Fluent in German and good English skills.
Nice-to-Haves
- Experience in revenue management or dynamic pricing, e.g., hotel, travel, eCommerce, or mobility.
- Familiarity with seasonality, events, lead times, and segment patterns.
- Hands-on experience with analytics engineering or warehouse tools such as dbt, Snowflake, or Metamorph.
- Bonus for hands-on skills working with MLOps tooling and cloud infrastructure, e.g., AWS.
