We are allies of the physician throughout their entire journey and we are looking for people who identify with our purpose, values and culture to join our team. Let’s transform healthcare together with those who have medicine as a vocation! This is an open position in the Data area, to work remotely. As a Mid-level Data Analyst at Afya, your primary responsibility will be to perform data analysis and modeling, and to develop dashboards and metrics in Power BI, ensuring the quality, clarity and governance of information.

• Own the strategic direction of the device data and performance platform • Define how Palmetto connects with hardware manufacturers through integration partnerships • Shape data models for performance monitoring and alerting • Build workflows for field service and asset management teams • Collaborate across Asset Management, Field Service, Operations, Customer Support, Finance, and Engineering • Drive alerting logic and performance insights • Ensure monitoring coverage with new asset types • Own product strategy, OKRs, roadmap, and quarterly planning • Encourage AI use for discovery and learning • Manage stakeholder expectations with clear communication

• Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products. • Develop contextualized feature sets using specific domain knowledge in soccer. • Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models. • Strive to constantly improve model performance using insights from rigorous offline and online experimentation. • Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts. • Adhere to software engineering best practices and contribute to shared code repositories. • Document modeling work and present to stakeholders and other technical and non-technical partners.

• Design, build, and validate predictive models (e.g. lead scoring, propensity modelling, forecasting, risk prediction) that inform operational and strategic decisions. • Develop and deploy generative AI solutions, including LLM-powered agents, retrieval-augmented generation (RAG) pipelines, and prompt engineering workflows to automate and enhance business processes. • Translate business problems into well-scoped analytical and modelling projects, working with stakeholders to define success criteria and measurable outcomes. • Perform feature engineering, model selection, hyperparameter tuning, and rigorous evaluation using appropriate statistical and ML techniques. • Productionise models into scalable, maintainable pipelines, collaborating with data engineering to integrate outputs into downstream systems (e.g. CRM, BI tools, operational dashboards). • Monitor model performance post-deployment, manage model drift, and implement retraining strategies. • Communicate findings and model outputs to non-technical stakeholders through clear visualisations, written summaries, and presentations. • Stay current with developments in applied ML and generative AI, and contribute to the team’s knowledge-sharing and capability-building efforts. • Provide guidance, direction, and oversight to one direct report within the Data, Insights & AI team.