• Leading the design, development, and deployment of AI/ML models for remote diagnostics, predictive maintenance, and operational optimization. • Analyzing large-scale machine and service datasets to uncover actionable insights and inform product improvements. • Collaborating with cross-functional teams including engineering, product management, and MLOps to integrate AI solutions into commercial applications. • Appling statistical, machine learning, and optimization techniques to solve complex healthcare challenges. • Developing and operationalizing GenAI solutions, including RAG architectures and AI agents using AWS, Azure, and open-source tools. • Ensuring scalability, reusability, and high-quality standards across AI products and pipelines. • Communicating technical findings and strategic recommendations to stakeholders across business and technical domains. • Mentoring junior team members and promote a culture of data-driven decision-making and continuous learning.

• Create the analytical foundation that powers pricing decisions across the company. • Develop frameworks and datasets that allow teams to understand price sensitivity, margin impact, and marketplace behaviour. • Build visibility into how pricing performs across different product categories and services. • Introduce experimentation into pricing decisions. • Design and analyse experiments that measure elasticity, conversion impact, and behavioural changes across pricing structures. • Work with Product and Engineering across all domains to implement safe and scalable experimentation. • Ensure pricing decisions across teams work together. • Work with leaders across Marketplace, Fulfilment, and Fintech to identify inconsistencies, conflicts, and opportunities. • Create a shared framework that helps teams understand trade offs between margin, liquidity, and growth. • Partner with Data and Engineering to move pricing logic into scalable systems. • Define the models, signals, and infrastructure required for dynamic or rules based pricing. Ensure pricing inputs can be integrated into the product platform over time. • Translate complex analysis into decisions leaders can act on. • Provide clear insight into pricing behaviour across the platform. • Help teams understand the trade offs between margin, liquidity, growth, and customer behaviour.

• Define and own the Foundation’s data strategy—what we measure, why it matters, and how it informs decisions across the organization • Lead a team of analysts and analytics engineers, each owning a specific ecosystem vertical, and synthesize their work into a coherent cross-vertical perspective • Own relationships with the Foundation’s ecosystem data providers, ensuring coverage, quality, and methodological rigor • Drive the data infrastructure roadmap and make decisions on tooling, gaps, and new partnerships • Represent the Foundation’s data perspective in executive conversations and external forums

• Apply statistical techniques (regression, distribution analysis, hypothesis testing) to derive insights from data and create advanced algorithms and statistical models such simulation, scenario analysis, and clustering • Explain complex models (e.g., RandomForest, XGBoost, Prophet, SARIMA) in an accessible way to stakeholders • Visualize and present data using tools such as Power BI, ggplot, and matplotlib • Explore internal datasets to extract meaningful business insights and communicate results effectively and write efficient, reusable code for data improvement, manipulation, and analysis • Manage project codebase using Git or equivalent version control systems • Design scalable dashboards and analytical tools for central use • Build strong collaborative relationships with stakeholders across departments to drive data-informed decision-making while also helping in the identification of opportunities for leveraging data to generate business insights • Enable quick prototype creation for analytical solutions and develop predictive models and machine learning algorithms to analyze large datasets and identify trends • Communicate analytical findings in clear, actionable terms for non-technical audiences • Mine and analyze data to improve forecasting accuracy, optimize marketing techniques, and informed business strategies, developing and managing tools and processes for monitoring model performance and data accuracy • Work cross-functionally to implement and evaluate model outcomes