🕒 5 days ago
Design, evaluate, and deploy machine learning systems for real-time data security, from NLP and LLM model research to scalable MLOps and production services.
Senior Machine Learning Engineer (m/f/d)
📅 Sep 15
Machine LearningForecastingPricing ModelsTime Series
📜 Description
- Develop and optimize forecasting, pricing, and data-driven decision models.
- Analyze time series, demand signals, and varied data sources; define features and labels to prevent leakage.
- Evaluate models with backtesting, robust metrics, segmentation, holdouts, and A/B testing.
- Improve standards for reproducibility, backtesting, and versioning, with engineering-quality implementations.
- Build dashboards and reports that clarify model and business KPIs.
- Create reliable pipelines, tests, and automated analysis and evaluation workflows.
🛠️ Requirements
- 4+ years of experience in data science or applied ML engineering, ideally in a product or business context.
- Strong SQL skills and expertise in data quality, debugging, and consistent metrics.
- Write clean Python code and produce transparent analyses.
- Understand bias and leakage risks, guardrails, and offline-versus-online evaluation.
- Take ownership, communicate clearly, and work pragmatically and reliably.
- Communicate fluently and confidently in English.
- Experience with revenue management or dynamic pricing and knowledge of seasonality, events, lead times, or segment patterns are advantageous.
- Experience with dbt, Snowflake, MLOps tooling, or cloud infrastructure such as AWS is a plus.
Full job description
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
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