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About the Role

You will be responsible for the data science work behind our cash flow forecasting and risk estimation system for artist royalty portfolios. This includes the complete development, evaluation, and continuous improvement of ML models – from raw data to investor-ready reports and production-ready APIs – in close collaboration with internal stakeholders and investors.

Responsibilities

• Design, train, and evaluate models for forecasting cash flow distributions over multi-year horizons.
• Develop and improve risk estimation methods.
• Conduct thorough model evaluation.
• Research and integrate additional public, freely available, or purchasable data sources to enhance model performance.
• Elicit, question, and refine analytics and ML requirements with business departments.
• Create and maintain investor-ready evaluation documents: KPI definitions, methodology descriptions, and result presentations with diagrams.
• Translate complex probabilistic model outputs into understandable business metrics.
• Transfer forecasting and risk models into REST APIs and integrate them into proprietary frontends.
• Maintain reproducible ML pipelines, including feature engineering, model serialization, and versioning.
• Connect to cloud data infrastructures.
• Develop LLM-based agentic workflows for automated data collection, harmonization, and enrichment.
• Integrate these workflows into production-ready APIs within the existing forecasting stack.

Requirements

• Python data science stack: pandas, Polars, scikit-learn, XGBoost (or comparable).
• End-to-end ML implementation: from exploratory data analysis and feature engineering to deploying a model that serves real users or decisions – applying MLOps best practices.
• Probabilistic modeling & quantile regression: Quantile Regression, calibration methods, or uncertainty quantification in predictions.
• Evaluation quality: Design of cross-validations, calibration analysis, and transfer of model metrics into business KPIs.
• Cloud infrastructure: practical experience with AWS, Azure, or GCP; proficient use of managed databases and deployment of ML services.
• Strong communication skills: ability to write clear methodology documentation and prepare results for non-technical audiences – including investors and management.

Skills

• Python
• pandas
• Polars
• scikit-learn
• XGBoost
• MLOps
• Probabilistic Modeling
• Quantile Regression
• Uncertainty Quantification
• Cross-validation
• Calibration Analysis
• AWS
• Azure
• GCP
• Managed Databases
• ML Service Deployment
• REST APIs
• LLM
• Agentic Workflows
• Bayesian Modeling
• Time Series Forecasting
• Cash Flow Forecasting
• Financial Risk Modeling
• NGBoost
• Distribution Regression
• LangChain
• LlamaIndex
• Anthropic SDK
• Streamlit
• Music Royalties
• Music Licensing
• Media Rights Exploitation
• GEMA
• ASCAP
• German Language

Location

• Berlin

Work Type

• Flexible working models
• Partial remote work

Experience Level

• Professional

Benefits

• Competitive compensation package
• Flat hierarchies
• Relaxed working environment

About the Company

• twelve x twelve is a Berlin-based startup that offers music rights holders an innovative way to gain liquidity from their future royalties. With our product twelve x twelve Advance, we transform the complex world of music finance into a digital, efficient, and scalable process. If you want to be part of a dynamic team that is revolutionizing the music industry, you've come to the right place!