About The Position
This role involves technical leadership and end-to-end ownership of our Pricing/Revenue ML initiatives, with a strong focus on measurable impact. You will collaborate closely with Product and Engineering teams to define measurability and experiments, ensuring our models perform reliably in practice. While there is no direct disciplinary responsibility, leadership is exercised through expertise, standards, and ownership.
Requirements
- Deep Experience: 5+ years of relevant experience in ML Engineering, Data Science, or Analytics, or a convincing equivalent track record.
- Proven Impact: Demonstrable success in Pricing, Revenue, Forecasting, or similar 'money systems'.
- Evaluation Pro: Proficiency in thinking about offline vs. online evaluation, immediate recognition of bias/leakage, and mastery of robust metrics and guardrails.
- Tech-Stack: Production-level Python and SQL skills (testable, versioned, reproducible).
- Startup DNA: Affinity for the 80/20 principle, pragmatic approach, and desire for full ownership.
- Language Skills: Fluent communication in German and proficient in English.
Nice To Haves
- Domain Knowledge: Experience in Revenue Management or Dynamic Pricing (e.g., Travel, Mobility, eCommerce).
- Demand Understanding: Knowledge of how seasonality, events, and lead times influence pricing.
- Modern Toolchain: Proficiency in Analytics Engineering (dbt, Snowflake, Metabase) and building a clean data foundation.
Responsibilities
- End-to-End Ownership: Take responsibility for the entire lifecycle of Pricing and Revenue ML topics, from hypothesis generation through implementation to measurable evaluation, with a clear focus on business uplift.
- Smart Modeling: Develop and optimize forecasting and pricing models, pragmatically deciding on the most efficient and stable methods to achieve goals.
- Signal Expertise: Manage time series, demand signals, and heterogeneous data sources, ensuring features and labels are defined cleanly and are 'leakage-proof'.
- Experimentation Framework: Build a robust measurement system (holdouts, A/B tests, guardrails) and define clear criteria for rollout decisions.
- Engineering-Grade ML: Establish standards for backtesting, reproducibility, and versioning, emphasizing engineering quality over notebook-only solutions.
- Reliable Operations: Ensure operational stability through smart monitoring, drift detection, and pragmatic retraining mechanisms.
- Automation & Scale: Automate high-leverage processes (backtests, monitoring checks) to significantly increase throughput and quality.
- Data Foundation: Design data models directly in the warehouse (Snowflake/dbt) where appropriate, serving as a basis for reliable metrics and features.
- Full Transparency: Standardize dashboards (e.g., Metabase) for business KPIs and ensure data quality is beyond reproach.
- Stakeholder Sparring: Prioritize requirements jointly with Product & Revenue teams and translate them into ML solutions, focusing on impact over output.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
