Responsibilities
- Develop portfolio optimisation and customer lifetime value models
- Analyse customer behaviour, profitability drivers, and retention patterns
- Apply statistical modelling, machine learning, and optimisation techniques
- Translate strategic business questions into structured analytical solutions
- Prepare and validate datasets for modelling and scenario analysis
- Collaborate with finance, pricing, and strategy stakeholders
- Expose analytical outputs to downstream systems and LangChain / LangGraph pipelines
- Ensure model robustness, consistency, and governance alignment
Basic qualifications
- Strong hands-on experience in Data Science and advanced analytics Proficiency in Python and common data science libraries (Pandas, NumPy, scikit-learn)
- Proficiency in Python and common scientific libraries (Pandas, NumPy, SciPy, scikit-learn)
- Experience with time series modelling and optimisation techniques
- Strong SQL skills and ability to work with large analytical datasets
- Experience working on analytics platforms such as Databricks or similar
- Ability to translate business objectives into quantitative models
- Experience working in structured enterprise environments
- Fluent English for professional collaboration
Preferred qualifications
- Experience in banking, financial services, or portfolio analytics
- Exposure to pricing, capital allocation, or customer value modelling
- Experience integrating analytical outputs into automated decision workflows
- Familiarity with LangChain and LangGraph for analytical orchestration
Benefits
- Solid, competitive salary
- Work in a multinational environment on international projects
- Comprehensive healthcare
- Long-term B2B contract with a stable project pipeline
- Remote work model
Tags & focus areas
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