• 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.

• Own and evolve the Data Science strategy across multiple product areas, translating company and product objectives into a clear, prioritized roadmap with measurable business impact. • Drive portfolio-level prioritization, balancing near-term delivery with longer-term platform and capability investments. • Set technical direction and standards for production ML systems, with end-to-end ownership from problem framing and data strategy through deployment, monitoring, and iteration. • Partner closely with Product, Engineering, Clinical, and Operations leaders to embed data science into product workflows and decision-making. • Represent Data Science in product planning and executive forums, communicating tradeoffs, risks, and impact to senior stakeholders. • Lead and scale the Data Science organization, including managers, senior ICs, and emerging leaders, with clear expectations and ownership. • Own hiring, onboarding, and talent development, building durable career paths and a high-performing organization. • Champion responsible, interpretable, and clinically sound AI, ensuring models are appropriate for healthcare use and real-world deployment.