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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in causal machine learning solutions, particularly in price elasticity estimation, with strong proficiency in Python and statistical methods. Capable of translating complex business questions into actionable ML/AI solutions while effectively communicating insights to diverse stakeholders.
Highest-signal resume keywords
Causal Machine LearningPython ProgrammingStatistical MethodsPrice Elasticity ModellingCloud ML Platforms (AWS)
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningAI ApplicationsData Processing in SparkModel Interpretability (SHAP)Hyperparameter Optimization (Optuna)Data Validation (Pandera, Pydantic)Time-Series AnalysisPanel Data AnalysisModel MonitoringData Quality Detection
Soft Skills
Excellent Communication SkillsIndependent and Collaborative Work
Tools & Technologies
PandasNumPyScikit-LearnXGBoostMLflowStreamlitDashTableauPowerBIDocker
Industry Keywords
PricingRevenue ManagementAutomotive IndustryConsulting/Service IndustryExperiment Tracking
Tech Stack
Tools & technologiesAWSCloudDockerNumpyPandasPythonScikit-LearnSparkTableau
About the role
Key responsibilities & impact- Build and evolve causal ML solutions for pricing, particularly price elasticity estimation with Double Machine Learning (DML).
- Collaborate with data engineering, pricing experts, and client stakeholders.
- Take end-to-end ownership of analyses, translating client business questions into well-identified ML/AI solutions.
- Own periodic model recalibrations across markets and brands.
- Rerun pipelines on new data windows, compare results against previous runs, and explain results.
- Extract valuable information from large, heterogeneous and imperfect data sources.
- Detect data-quality problems.
- Evaluate model performance and iteratively improve accuracy and efficiency.
- Communicate findings and insights to technical and non-technical stakeholders.
- Contribute to a production-grade, config-driven Python codebase using testing, typing, code review, spec-driven development, and AI-assisted coding.
Requirements
What you’ll need- Bachelor's degree or higher in Computer Science, Statistics, Mathematics, Physics, Economics/Econometrics, or a related quantitative field.
- At least 4 years of relevant work experience.
- Proven experience in machine learning and AI, with a focus on practical applications.
- Strong proficiency in Python and the scientific stack (pandas and/or polars, NumPy, scikit-learn, statsmodels, gradient boosting such as XGBoost), writing clean, tested, maintainable code (pytest, type hints, linting).
- Strong understanding of statistical methods and concepts.
- Experience with time-series and panel data, including seasonality, outliers and structural breaks.
- A rigorous, hypothesis-driven approach to analysis: forming and testing hypotheses with reproducible scripts and reporting negative or inconclusive results honestly.
- Ability to work independently and collaboratively in a fast-paced environment.
- Excellent communication skills with the ability to present complex technical concepts clearly.
- Hands-on experience with cloud ML platforms, ideally AWS (SageMaker, S3), and experiment tracking (e.g. MLflow).
- Fluent English
- Domain knowledge in pricing, revenue management, or the automotive industry (list vs. transaction prices, discounts and incentives, financing/leasing, model lifecycles).
- Experience with price elasticity / demand modelling or causal ML libraries (e.g. EconML, DoubleML).
- Experience with model interpretability (SHAP), hyperparameter optimization (Optuna) and data validation (pandera, Pydantic).
- Experience with model monitoring, recalibration and data/concept drift analysis.
- Experience with data processing in Spark and Databricks.
- Experience with data visualization, dashboarding tools or simple frontends for visualization (Streamlit, Dash, Tableau, PowerBI).
- Experience deploying ML/AI models into production environments.
- Familiarity with Git-based workflows, Poetry, pre-commit, CI, containerization (Docker), and AI coding assistants / spec-driven development.
- Ability to write clear technical reports (Markdown, LaTeX).
- Previous experience in the consulting/service industry.
Benefits
Comp & perks- Training programs
- Free language courses
- Competence centers
- Active tech community
- Hybrid work
- Workation
- Flexible hours
- Parental support
- Sabbaticals
- Sustainability Hub
- Diverse communities
- Diversity Taskforce
- After-work activities
- Public transport tickets
- Job bikes
- Access to exclusive Volkswagen employee conditions when leasing or purchasing a vehicle
- Health offers
- Supplementary insurances
- Pension plan
- Various discounts