Staff Machine Learning Engineer - Pricing & Revenue (m/f/d)
At a glance
Senior Machine Learning Engineering role at happyhotel. Offenburg · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Own pricing and revenue ML from initial hypothesis through implementation, then measure its business impact
- Build and tune forecasting and pricing models, choosing methods for speed and stable performance
- Define clean features and labels from time series, demand signals and varied data sources, preventing leakage
- Set up holdouts, A/B tests and guardrails, with clear rules for deciding when models are ready to roll out
- Improve production ML through backtesting, versioning, monitoring, drift checks, retraining and automated workflows
What you bring
- At least six years in applied ML engineering or data science, ideally in a product or business setting
- Evidence of results in pricing, revenue, forecasting or a comparable money-focused area
- Production-ready Python and SQL skills, with testable, versioned and reproducible work
- Fluency in English and a strong grasp of evaluation, including online versus offline metrics and leakage risks
Who this fits
This suits a senior individual contributor who leads through technical judgment and ownership rather than managing staff. You’ll work closely with Product, Engineering and Revenue, and the role is hybrid in Offenburg, Germany.
From the employer
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.