Pricing ML Engineer for Data-Driven Revenue Growth

Happyhotel; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

happyhotel is seeking a Senior Machine Learning Engineer to join the Pricing & Revenue team in the United States. You will develop data-driven forecasting and pricing logic, create insights, and collaborate with Product & Engineering to drive measurable product impact.

You will evolve forecasting models, define features from time-series signals, and ensure robust evaluation with backtesting and guardrails. A strong data foundation and reproducible workflows are essential.

Qualifications

• 4+ years of experience in data science or applied ML engineering, ideally in a product context.
• Strong SQL skills and a deep sense for data quality and metrics.
• Proficient Python coding with transparent analyses; product-oriented setup a plus.
• Fluent in German with good English skills.
• Ownership mindset and ability to work pragmatically (80/20).
• Entrepreneurial spirit and willingness to make a difference.

Responsibilities

• Model evolution: develop and optimize forecasting and pricing models with measurable impact.
• Signal hunting: work with time series, demand signals, and heterogeneous data.
• Measurement & guardrails: evaluate with backtesting, robust metrics, segmentation; support A/B logic.
• ML engineering best practices: improve backtesting, reproducibility, and versioning.
• Data visibility: improve dashboards and reports for KPI transparency.
• Smart workflows: drive reproducible pipelines and automate analyses.

Skills

SQL

Python

Experimentation

Ownership

Communication

Tools

dbt

Snowflake

MLOps

AWS

Job description

happyhotel is seeking a Senior Machine Learning Engineer to join the Pricing & Revenue team in the United States. You will develop data-driven forecasting and pricing logic, create insights, and collaborate with Product & Engineering to drive measurable product impact.

You will evolve forecasting models, define features from time-series signals, and ensure robust evaluation with backtesting and guardrails. A strong data foundation and reproducible workflows are essential.