Preparing for an interview at SIXT requires a balanced approach. You cannot rely solely on your coding skills, nor can you get by on pure business intuition. The hiring team looks for well-rounded practitioners who can write clean code, explain the underlying math, and connect their models to business value.

Technical Rigor – You must have a strong grasp of machine learning theory. Expect interviewers to push past the surface level of your past projects. You should be ready to write clean Python code, design robust validation strategies, and discuss the mathematical trade-offs of different algorithms.

Product and Business Acumen – SIXT is a highly commercial business. You need to demonstrate that you do not build models in a vacuum. Be prepared to discuss how your data science solutions impact operational costs, customer retention, and revenue generation, particularly concerning dynamic pricing and supply-demand matching.

Structured Problem Solving – When faced with ambiguous case studies, such as designing a pricing engine, do not jump straight to an algorithmic solution. Start by structuring the problem, identifying key constraints (e.g., fleet size, seasonal demand), defining success metrics, and then proposing an iterative modeling approach.

Clear Communication – You will collaborate with engineering, product, and regional business leaders. The ability to translate complex machine learning concepts into actionable business strategies is a critical evaluation criterion during the leadership and HR rounds.

The recruitment pipeline for a Data Scientist at SIXT is a comprehensive, multi-stage process designed to evaluate your technical execution, theoretical depth, and strategic alignment. While the team is highly professional and technically inspiring, candidates should prepare for a thorough process that typically spans four to eight weeks.

The journey begins with a standard recruiter phone screen to assess your background and motivation. This is followed by a rigorous take-home assignment that serves as the primary technical gatekeeper. Once your submission is accepted, you will transition into a series of technical and managerial interviews that dig deep into your coding choices, theoretical knowledge, and system design capabilities.

This timeline illustrates the typical progression from your initial application to the final offer stage. Candidates should use this visual guide to manage their preparation energy, focusing heavily on coding and visualization skills early in the process, before shifting their attention to machine learning theory, system design, and behavioral alignment in the later rounds.

Take-Home Assignment & Coding

The take-home assignment is a critical component of the SIXT evaluation process. It simulates a real-world data science task, typically giving you three to seven days to complete and return a Python-based solution.

The test is highly structured and evaluates your ability to clean data, perform exploratory data analysis, construct meaningful visualizations, and implement a predictive model. For example, you may be asked to plot specific distributions over time or use a pre-determined algorithm to establish a baseline model.

Be ready to go over:

• Data Visualization – Writing clean code to generate clear, interpretable plots (such as demand distributions over time) that a business stakeholder could easily understand.
• Model Implementation – Implementing machine learning algorithms cleanly, demonstrating proper data splitting, feature engineering, and hyperparameter tuning.
• Code Quality – Organizing your Jupyter notebook or Python scripts with clear comments, modular functions, and professional documentation.

Example scenarios:

• "Plot the distribution of hourly rental bookings over a three-month period and explain any seasonal trends you observe."
• "Implement a regression model using a gradient-boosted tree to predict fleet utilization, justifying your choice of evaluation metrics."

Machine Learning & Algorithmic Theory

Once you pass the take-home challenge, you will face a dedicated technical interview that evaluates your theoretical understanding. Even if your assignment code is excellent, you must be prepared to defend the underlying mathematics of your models.

Interviewers will drill down into specific algorithms mentioned in your resume or used in the take-home test. They want to ensure you understand the mechanics of the tools you use, rather than just importing libraries.

Be ready to go over:

• Algorithmic Mechanics – Deeply understanding how specific models split nodes, optimize loss functions, or update weights.
• Model Evaluation – Explaining why certain metrics (e.g., Precision-Recall AUC vs. ROC AUC) are appropriate for specific business problems.
• Natural Language Processing (NLP) – Understanding text representation techniques, word embeddings, and transformer architectures if applying for specialized or Gen AI roles.
• Advanced concepts – Deep learning architectures, vector databases, and fine-tuning methodologies for large language models.

Example scenarios:

• "Walk me through the mathematical difference between L1 and L2 regularization and how they affect model weights."
• "In the algorithm you used for the take-home test, how does the model handle missing values during training and inference?"

Pricing Strategy & Mobility Case Studies

Because SIXT operates in the highly dynamic mobility space, a core part of the technical evaluation involves interactive business case studies, with a strong emphasis on pricing and fleet optimization.

You will often be interviewed by senior data scientists or heads of data who will present you with an ambiguous business challenge and ask you to design an end-to-end data science solution.

Be ready to go over:

• Dynamic Pricing Systems – Designing algorithms that adjust rental prices based on real-time demand, competitor pricing, and local fleet availability.
• Ranking & Recommendation Systems – Structuring models that optimize the display of available cars to maximize conversion rates.
• Experimental Design – Setting up robust offline and online evaluation frameworks to measure the business impact of your models.

Example scenarios:

• "Design a pricing strategy for a new utility vehicle segment in a major metropolitan area where historical data is sparse."
• "How would you design an algorithm to balance the trade-off between displaying highly relevant 'top results' and fresh 'trending results' to a user searching for a car?"

On a day-to-day basis, a Data Scientist at SIXT is responsible for building the intelligent systems that power global mobility operations. You will not work in isolation; instead, you will collaborate closely with product managers, software engineers, and business leaders to turn raw data into automated, high-impact decisions.

Your primary focus will be on designing, training, and deploying machine learning models to production. This includes writing clean, scalable Python code, setting up automated data pipelines, and continuously monitoring model performance in real-world conditions. You will work on core algorithms that determine rental pricing, forecast demand across airports and downtown stations, and personalize the digital booking experience for millions of customers.

For teams focused on emerging technologies, such as Generative AI, your responsibilities will extend to building and integrating large language models. This involves fine-tuning foundation models, designing retrieval-augmented generation (RAG) pipelines, and developing intelligent agents to automate complex customer service workflows and internal operations.

To be competitive for a Data Scientist position at SIXT, you must demonstrate a strong blend of technical expertise, practical experience, and business-focused communication.

Must-Have Skills

• Strong proficiency in Python and its data science ecosystem (pandas, numpy, scikit-learn, matplotlib/seaborn).
• Solid theoretical understanding of machine learning algorithms, statistical modeling, and experimental design (A/B testing).
• Proficiency in SQL for querying and manipulating large, complex datasets.
• Track record of building and deploying machine learning models that solve actual business problems.
• Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.

Nice-to-Have Skills

• Experience working in the mobility, travel, on-demand transport, or logistics industries.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization tools like Docker.
• Experience with advanced deep learning, natural language processing, or Generative AI/LLM frameworks.
• Prior experience building dynamic pricing, revenue management, or yield optimization systems.