Senior Machine Learning Engineer (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
- Improve forecasting and pricing models, then turn their outputs into practical, data-based product decisions
- Build features from time series, demand signals and mixed data sources while preventing information leakage
- Check model quality with backtests, robust metrics and segments, and help assess holdouts and A/B tests
- Strengthen reproducibility, version control and testing, while automating repeatable analysis and evaluation
- Make model and business KPIs easier to track through dashboards and reports grounded in quality data
What you bring
- At least four years in data science or applied machine learning engineering
- Strong SQL skills, sound judgment about data quality, and the ability to debug inconsistent metrics
- You write clear Python and understand bias, leakage and the tradeoffs between offline and online results
- You communicate confidently in English
Who this fits
This senior role suits someone who takes ownership, works pragmatically and communicates clearly while collaborating with Product and Engineering. It is based in Offenburg, Germany, with a hybrid arrangement.
From the employer
Your Role
As a Senior Machine Learning Engineer, you will work in the Pricing & Revenue context, focusing on data-driven improvements for forecasting, pricing logic, and product insights. You will develop analyses and models that demonstrate measurable impact in our product – with thorough evaluation, a solid data foundation, and pragmatic implementation. You will collaborate closely with Product & Engineering.
Your Responsibilities
Model Evolution: You will develop and optimize our forecasting and pricing models as well as data-driven decision logics, always with methodical pragmatism and a strong focus on impact.
Signal Hunting: You will work with time series, demand signals, and heterogeneous data sources. You define features and labels carefully to leave no chance for leakage.
Measurement & Guardrails: You are responsible for evaluation through backtesting, robust metrics, and segmentation. You support holdouts and A/B logics and maintain the balance between offline and online performance.
ML Engineering Best Practices: You raise standards for backtesting, reproducibility, and versioning. For us, it's: Engineering quality instead of notebook-only.
Data Visibility: You enhance dashboards and reports that make model and business KPIs transparent. Your focus is always on the highest data quality.
Smart Workflows: You drive reproducible workflows (versioning, clear pipelines, meaningful tests) and automate recurring analyses and evaluation runs.
Your Profile
Track Record: You have 4+ years of experience in data science or applied ML engineering – ideally directly in a product or business context.
Data Intuition: You possess extremely strong SQL skills and a deep sense for data quality, debugging, and consistent metrics.
Python Pro: Your Python code is clean and your analyses are transparent. Initial experience with product-oriented setups is a big plus.
Experiment Mindset: You master the basics of bias/leakage-awareness and know how to think in guardrails and offline-vs-online scenarios.
Work Style: You take full ownership of your topics. You work according to the 80/20 principle (pragmatic!), are reliable, and communicate clearly.
Entrepreneurial Spirit: You think entrepreneurially and want to truly make a difference.
Language Skills: You communicate fluently and confidently in English.
Nice-to-haves – What Sets the Best Apart
Domain Expertise: You already have experience in revenue management or dynamic pricing (e.g., hotel, travel, eCommerce, or mobility).
Demand Knowledge: You are familiar with seasonality, events, lead times, and segment patterns.
Modern Stack: You have already worked with analytics engineering or warehouse tools such as dbt, Snowflake, or Met
Hands-On MLOps & Cloud: Bonus if you have hands-on skills to work with MLOps tooling and cloud infrastructure, e.g. AWS