• Design and develop user-facing analytic applications and dashboards in Palantir Foundry • Translate complex data models and analytics into clear, compelling visualizations • Build interactive, data-driven UIs that embed analytics and metrics into workflows • Collaborate with data scientists and engineers to integrate analytics, AI outputs, and ontologies • Apply UX/UI best practices to deliver accessible, intuitive, and scalable applications • Collaborate in an Agile/Scrum environment to rapidly iterate on solutions • Communicate insights effectively to technical and non-technical stakeholders

• Conduct data discovery and exploratory data analysis (EDA) across structured and unstructured data • Perform data cleansing, normalization, aggregation, and modeling • Design and build analytic applications in Palantir Foundry, including dashboards and interactive workflows • Apply advanced analytics and statistical methods (e.g., regression, clustering, hypothesis testing, A/B testing) • Develop and embed AI/ML capabilities (e.g., LLMs, NLP, RAG/GraphRAG) into analytic workflows • Create clear visualizations and metrics to support executive and operational decision-making • Collaborate in an Agile/Scrum environment to rapidly iterate on solutions • Communicate insights effectively to technical and non-technical stakeholders

• Perform comprehensive evaluations of data assets across the full credit and fraud lifecycle, ensuring strong regulatory compliance and risk governance. • Conduct rigorous benchmarking and champion/challenger analyses to compare external data assets against internal attributes, scores, and predictive models, ensuring measurable lift, ROI and benefits. • Develop advanced machine learning solutions and algorithms to solve complex analytical challenges. • Lead the design, development, and deployment of advanced AI/ML systems with a focus on multi-agent and intelligent systems. • Collaborate cross-functionally with engineering, product, and analytics teams to translate cutting-edge research into impactful solutions. • Stay current with advancements in AI/ML and proactively integrate emerging technologies into Experian's solutions.

• Build, evaluate, and deploy machine learning models for cybersecurity risk scoring, threat intelligence, and vendor risk assessment. • Perform exploratory data analysis and identify patterns, trends, and anomalies in large and complex datasets. • Evaluate and validate models, ensuring their accuracy, robustness, and scalability. • Collaborate with product managers and engineers to define data science requirements and integrate models into production systems. • Contribute to code reviews, design discussions, and data science best practices. • Stay current on new research and technologies to ensure we leverage state-of-the-art methods. • Communicate findings and recommendations effectively to both technical and non-technical audiences.