• Lead and develop a high-performing Data Science and Analytics Engineering team • Drive data-led product improvements across growth, engagement, and monetization • Partner closely with Product, Engineering, and Leadership to shape strategy • Build systems and frameworks that improve decision speed and clarity • Establish clear and trusted metrics and data definitions • Integrate AI into data workflows to improve efficiency and insight generation • Maintain a high hiring bar and strong team culture

• Leverage large and complex datasets to explore, discover, and identify patterns, trends, and insights that can influence strategic business decisions. • Develop, validate, and deploy predictive models and machine learning algorithms to solve business problems. • Write code to automate data processing and aggregation tasks. • Collaborate with cross-functional teams to understand business needs and provide data-driven solutions. • Present findings in a clear and easily understandable way to non-technical stakeholders. • Work closely with both onshore and offshore teams to deliver data-driven projects on time and to specification. • Continuously learn new technologies and stay up-to-date with the latest industry trends in data science.

• Develop and implement real-time scoring models to quantify the risk level of transactions and credit operations. • Build predictive models using internal and third-party data to optimize user onboarding and reduce losses. • Evolve static rule engines into dynamic, graph-based systems, enabling smarter and more adaptable rule management. • Lead the adoption of new technologies such as Databricks and Data Catalog, advocating best practices and facilitating the transition to a more modern and efficient data environment. • Analyze large volumes of transactional, user-behavior, and demographic data to identify patterns, trends, and opportunities to improve risk assessment. • Develop and implement geolocation and fingerprinting solutions to enhance risk evaluation. • Guide and mentor team members by sharing expertise and knowledge, and lead key projects from a technical perspective. • Monitor and analyze the performance of credit models, focusing on their stability and accuracy.

• Prototype innovative ideas, conduct experiments, and iterate quickly to develop data-driven solutions for complex business challenges • Independently work on end-to-end research projects while actively collaborating with a team of data scientists • Present and promote results to internal stakeholders and external clients and partners. • Support and improve existing data science products • Analyze large, complex, and noisy datasets to extract actionable insights and support data-driven decision-making • Design, implement and evaluate machine learning and statistical methods to perform exploratory data analysis, data storytelling and product development • Design and execute A/B tests and experiments to optimize product performance and maximize revenue growth • Pursue patents where applicable • Contribute to thought leadership in the community with presentations and publications