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Open nowPosted 227 days ago

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

happyhotel16 open roles

Where
Offenburg
Work mode
Remote
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Your applicationOpen nowStaff Machine Learning Engineer - Pricing & Revenue (m/w/d)happyhotel · Offenburg

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The clock on this job

Early applications get read.

7.4% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 1.5%1 day
  2. 3.5%3 days
  3. 7.4%7 days
  4. 13.2%14 days
  5. 34.6%30 days
This job: posted 227 days ago

The posting

DEINE ROLLE

Du übernimmst fachliche Führung und End-to-end Ownership für unsere Pricing/Revenue-ML-Themen — mit klarem Fokus auf messbaren Impact. Du arbeitest eng mit Product und Engineering zusammen, definierst Messbarkeit/Experimente, und stellst sicher, dass unsere Modelle nicht nur “gut aussehen”, sondern in der Praxis zuverlässig performen.

Wichtig: Keine disziplinarische Personalverantwortung. Du führst über Expertise, Standards und Ownership.

DEINE AUFGABEN

- End-to-End Ownership: Du verantwortest den gesamten Lifecycle von Pricing- und Revenue-Themen – von der Hypothese über die Umsetzung bis zur messbaren Evaluation. Dein Fokus: Klarer Business-Uplift.

- Smart Modeling: Du entwickelst und optimierst Forecasting- und Pricing-Modelle. Dabei entscheidest du pragmatisch, welche Methode uns am schnellsten und stabilsten zum Ziel führt.

- Signal-Expertise: Du bändigst Zeitreihen, Demand-Signale und heterogene Datenquellen. Du stellst sicher, dass Features und Labels absolut sauber und "Leakage-sicher" definiert sind.

- Experimentation-Framework: Du baust ein belastbares Mess-System auf (Holdouts, A/B-Tests, Guardrails) und definierst glasklare Kriterien für Rollout-Entscheidungen.

- Engineering-Grade ML: Du etablierst Standards für Backtesting, Reproduzierbarkeit und Versionierung. Bei uns heißt es: Engineering-Quality statt Notebook-only.

- MLOps Best Practices: Du bringst Best Practices im Bereich MLOps ein und treibst die kontinuierliche Verbesserung unserer ML-Workflows voran.

- Reliable Operations: Du sicherst den Betrieb durch smartes Monitoring, Drift-Erkennung und pragmatische Retraining-Mechanismen.

- Automation & Scale: Du automatisierst Prozesse mit hohem Hebel (Backtests, Monitoring-Checks), um Durchsatz und Qualität massiv zu steigern.

- Data Foundation: Wo es Sinn ergibt, designst du Datenmodelle direkt im Warehouse (Snowflake/dbt) als Basis für verlässliche Metriken und Features.

- Full Transparency: Du standardisierst Dashboards (z. B. Metabase) für unsere Business-KPIs und sorgst dafür, dass die Datenqualität über jeden Zweifel erhaben ist.

- Stakeholder-Sparring: Du priorisierst Anforderungen gemeinsam mit Product & Revenue und übersetzt sie in ML-Lösungen. Dein Motto: Impact vor Output.

DEIN PROFIL

- Deep Experience: Du bringst mindestens sechs Jahre Erfahrung im Bereich Applied ML Engineering oder Data Science mit – idealerweise direkt in einem Produkt- oder Geschäftskontext.

- Proven Impact: Du hast bereits nachweisbare Erfolge in den Bereichen Pricing, Revenue, Forecasting oder ähnlichen "Money-Systemen" erzielt.

- Evaluation-Pro: Du denkst in Offline-vs-Online, erkennst Bias/Leakage sofort und beherrscht das Einmaleins der robusten Metriken und Guardrails.

- Tech-Stack: Deine Python- und SQL-Skills sind auf Production-Niveau (testbar, versioniert, reproduzierbar).

- Startup-DNA: Du liebst das 80/20-Prinzip, arbeitest extrem pragmatisch und willst volle Ownership für deine Themen.

- Sprachkenntnisse: Du kommunizierst fließend und sicher auf Englisch.

BONUS POINTS (NICE-TO-HAVES)

- Hands-on MLOps & Cloud: Ein Plus ist praktische Erfahrung im Umgang mit MLOps-Tools und Cloud-Infrastruktur, z. B. AWS.

- Domain-Wissen: Erfahrung in Revenue Management oder Dynamic Pricing (z. B. Travel, Mobility, eCommerce).

- Demand-Verständnis: Du weißt, wie Saisonalität, Events und Lead-Times das Pricing beeinflussen.

- Modern Toolchain: Du bist fit in Analytics Engineering (dbt, Snowflake, Metabase) und weißt, wie man eine saubere Datenbasis baut.

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