Senior Data Scientist - Customer Lifetime Value

Hybrid in London / Remote within the UK

Is your CV ready If so, and you are confident this is the role for you, make sure to apply asap.We’re looking for a Data ScientistOur Customer Lifetime Value (CLtV) model is a highly developed and tailored model, which is central to our business strategy at iwoca. As a Data Scientist in our CLtV team, you will be developing this model so that it represents and codifies our best and most current understanding about the true lifetime value of our customers.The roleYour role as a Data Scientist in the CLTV team will involve growing and demonstrating your skills in several key areas, including but not limited to:Model development.Explore and integrate innovative modelling methods into our training pipeline to enhance the predictive power and flexibility of our model.

Take responsibility for the full lifecycle of the model, including training, validation, deployment, and performance monitoring.

Clearly communicate and explain any model changes to the business, ensuring transparency and fostering trust in the model's predictions.

Collaborate with Data Scientists and Analysts in other teams to ensure that our model predictions are appropriately utilised and interdependencies are accounted for.

Model-driven insights.Utilise our modelling and analytics tools (and introduce new ones where appropriate) to uncover insights, such as customer behaviour patterns or the efficacy of new modelling techniques.

Effectively communicate these insights with the broader business to drive value by changing the way iwoca operates.

Project ownership and autonomy.Independently develop data science solutions to address iwoca's business challenges, with increasing responsibility in solution design.

Maintain strong communications with stakeholders throughout your work to ensure that your solutions are pragmatically solving the business problem at hand and to get technical feedback for personal growth.

The requirementsEssential:Ability to effectively communicate with stakeholders and downstream users of the model, and to maintain up-to-date and reliable documentation.

Strong problem-solving skills in probability and statistics.

Experience developing code collaboratively and implementing solutions in a production environment.

Proficiency with data manipulation and modelling tools — e.g., pandas, statsmodels, R.

Experience with scientific computing and tooling — e.g., NumPy, SciPy, Matlab, etc.

Self-driven with the capability to efficiently manage projects end-to-end.

Experience working on research projects, particularly those involving mathematical, statistical, or analytical modelling.

Bonus:Experience building machine learning models from scratch (e.g., built your own optimiser).

Excellent knowledge of stochastic processes and related mathematical techniques.

Experience with Python. (Note: we mostly work in Python.)

Knowledge of financial concepts (e.g. calculations with deterministic cash flows).

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