• 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.

• Own and improve Jiffy’s product analytics foundation, with Mixpanel as the primary tool for funnels, conversion analysis, and experimentation insights. • Lead event tracking standardization by defining a clean, consistent taxonomy, naming conventions, and event governance practices. • Drive completion of the Rudderstack migration by building detailed tracking specifications and partnering with engineering teams on implementation. • Ensure events can be tied historically where possible, enabling reliable trend analysis over time. • Build and maintain clear conversion funnels and key customer journeys across the ecommerce experience. • Support A/B testing and product experimentation by providing trusted measurement frameworks and analysis. • Enable self-service analytics for product and UX teams, reducing dependency on ad-hoc SQL and one-off reporting. • Partner cross-functionally with Product, Engineering, and Design to ensure analytics is actionable, adopted, and embedded into product development workflows. • When needed, combine behavioral insights with broader business reporting using tools such as Hex.

• Own marketing measurement and performance reporting across channels including Google Ads, Meta, email, and affiliates. • Build scalable, executive-ready dashboards (primarily in Hex) that track ROI, CAC, LTV, payback period, and campaign performance across business lines. • Improve attribution and campaign performance visibility beyond basic first/last touch approaches, ensuring marketing reporting ties back reliably to orders and revenue. • Partner directly with channel owners to identify the right metrics, extract key fields from marketing platforms, and create consistent reporting foundations. • Lead performance optimization efforts for priority channels, with Google Ads as the primary initial focus. • Design and analyze incrementality and lift testing approaches (starting with geo lift studies) to measure true marketing impact. • Support faster campaign iteration through structured post-campaign analysis and ongoing performance insights. • Bring strong metric governance and discipline to ensure marketing teams and leadership can confidently act on performance data. • Communicate clearly with executives and stakeholders, including the ability to push back on misleading attribution signals.