You will own data science initiatives across Growth and Marketing, beginning with customer lifetime value prediction across markets and acquisition channels. You will turn ambiguous commercial questions into measurable modelling problems, analyse customer and transaction data, and develop solutions that influence growth, retention, and monetisation decisions. You will work with Growth, Marketing, Product, Finance, Data Engineering, and Engineering to productionise models, pipelines, and reporting. You will monitor model performance, improve accuracy and reliability, and communicate modelling assumptions, limitations, and recommendations clearly to stakeholders.
Responsibilities
- Design, build, maintain, and improve lifetime value prediction models across countries and acquisition channels.
- Translate business needs into modelling solutions with Growth, Marketing, Product, Finance, Data Engineering, and Engineering.
- Evaluate acquisition quality by channel, campaign, geography, customer segment, and product behaviour.
- Build models to inform growth spend, payback periods, customer quality, retention, and long-term value.
- Analyse customer, product, marketing, and transaction datasets to identify patterns, risks, and opportunities.
- Monitor model performance and improve accuracy, reliability, and business impact.
- Create frameworks and metrics for evaluating growth decision trade-offs.
- Partner with Data Engineering and Engineering to productionise models, pipelines, and reporting.
- Contribute to additional data science challenges across Growth and the wider business.
Requirements
- 5+ years of experience in data science, machine learning, applied statistics, analytics, or a related discipline.
- Experience building prediction models in a B2C or D2C consumer business.
- Strong Python skills and experience with large-scale datasets.
- Understanding of supervised learning, model evaluation, feature engineering, and statistical trade-offs.
- Ability to translate ambiguous commercial questions into structured data science problems.
- Business judgement that connects model outputs to decisions.
- Cross-functional working experience.
- Ability to explain modelling assumptions, limitations, and recommendations to non-technical stakeholders.
- Fluency in English.
Benefits
- Competitive salary and benefits.
- Stock options.
- Discretionary performance bonus.
- Latest tools and technology.
- A world-class team that will challenge and grow your skills.
- The opportunity to help build the best fintech app in Latin America.
- Office policy of 3–4 days per week in-office.